Abstract

     Frost and thawing damage asphalt in cold climates. Water that enters the pores of asphalt at low temperatures may accelerate its degradation. Water can expand into pores, altering the void content and shape, thus affecting the asphalt’s internal structure and fracture properties. We can develop more durable asphalt types if we understand how void topology changes with freeze–thaw cycles. The purpose of this study is to establish a correlation between the mechanical properties of asphalts and the topological properties of voids. To determine their internal structure, various asphalt types representing dense asphalt and asphalt with voids were made and X-rayed before and after each freeze–thaw cycle. We also obtained the mechanical properties of asphalt and correlated them with the void properties. It was found that dense asphalt has the lowest degradation rate in wet conditions characterised by non-connected gaps, which was approximately constant with freeze–thaw cycles; however, dense asphalt was least durable under dry conditions compared with asphalts with more voids. Due to its high water retention rate, asphalt with a 10% void content degraded at an accelerated rate during the initial cycles. As a result of bigger voids, asphalt with a higher pore content plateaued in later cycles due to reduced water retention.

Introduction

     Asphalt mixture is a heterogeneous material made of aggregates, mineral filler, and a bituminous binder. It is compacted at temperatures ranging between 135 °C and 155 °C; asphalt mixtures may also include voids, up to more than 20 % of the total asphalt’s volume, depending on the gradation and compaction methodology. Asphalt is the most used material to build pavement surfaces. Asphalt undergoes different mechanical loads and environmental conditions during its lifetime, leading to crack and damage and reducing its lifespan [1], [2]. In particular, the extreme environmental condition in cold regions with large temperature fluctuations [3] and the water permeation are some of the most harmful deterioration mechanisms that may affect asphalt, leading to thermal stress [4], loss of adhesion at the interface between aggregate and bitumen [5], and loss of cohesion of the binder [6].

Conclusions

     Several types of asphalt with acceptable air void contents ranging from 3% to 14% were examined for their influence during freeze–thaw cycles, where a correlation between differences in air void topology and changes in mechanical properties was established. According to the results in this study, the topological properties evolution under freeze–thaw cycles was influenced by three different phenomena: (1) volume increase of the existing voids and formation of small cracks, (2) voids coalescence, and (3) cracking and new voids formation. The voids were analysed using X-ray CT scans after every freeze–thaw cycle, and the mechanical properties were measured from 3-point bending tests. The following conclusions were obtained:

     The three different asphalts had different behaviours when exposed to freeze–thaw cycles, attributed to the different aggregate size distributions and air void topologies. Hence, the durability of asphalt to freeze–thaw cycles could be controlled by carefully selecting the aggregate gradation, aggregate morphology and air void topologies.

1- Introduction

2- System architecture

3- Modelling of the WECS

4- Performance analysis of proposed DC nanogrid topology

5- Conclusion

References

Abstract

Wind energy conversion system (WECS) based DC nanogrid topology is proposed in this paper. The WECS topology is based on self-excited induction generator (SEIG) which is cost effective, and has rugged construction and combination of diode bridge rectifier (DBR) with DC-DC converter to implement the maximum power point tracking technique. The combination of diode DBR with DC-DC converter allows simple control structure due to one controllable switch as compared to complex control in the conventional controlled/semi-controlled rectifier with six controllable switches. Further, proposed DC nanogrid topology enhances the SEIG efficiency due to the reduction of harmonic injection on machine side from DBR implementation. The detailed harmonic spectrum analysis is carried out by developing the laboratory scale prototype of DBR based SEIG topology for WECS to justify the selection of DBR instead of the controlled rectifier. Further, to analyze the effect of different types DC-DC converter to make combination with the DBR. Afterwards, the dynamic performance of the proposed DC nanogrid topology is analyzed to understand the selection of the DC-DC converter with DBR for different DC grid voltage conditions.

Introduction

Electricity plays a vital role in energy supply, however, the electricity generation is the single largest source of CO2 emission, which is more than 40% of CO2 emission related to global energy. By the year 2030, global electricity demand is likely to double [1,2]. Hence, climate change mitigation and increasing energy demand motivates for the development of renewable energy resources. The renewable energy resources such as solar and wind energy are distributed in nature which favors the electricity generation and consumption on site. With technological advancement and regulatory changes, the concept of supplying the load locally has led to a concept of nanogrid. Thus, the development of renewable based nanogrid are gaining attention in recent past for providing environment-friendly, sustainable and economical electricity [3]. Further, the development of power electronics has transformed the load profile of end user as the modern appliances operate on DC supply [4]. Having this understanding, the concept of DC nanogrid has been proposed in the literature to elude multiple AC-DC conversion and vice-versa [5]. The DC distribution enables easy control, no issue of frequency, power angle, phase sequence, and power factor. Mashood et al.

This work is devoted to the study of application of new topologies in the design of networks-on-chip (NoCs). It is proposed to use two-dimensional optimal circulant topologies for NoC design, and it is developed an optimized routing algorithm with the decreased memory usage. The proposed routing algorithm was compared with Table routing, Clockwise routing, and Adaptive routing algorithms, previously developed for ring circulant topologies, and specialized routing algorithm for multiplicative circulants. The results of synthesis of routers implementing proposed routing algorithms are presented. The cost of ALM and register resources for the implementation of communication subsystems in NoCs with circulant topologies is estimated.

Introduction

Multiprocessor systems-on-chip (MPSoCs) [1] are currently one of the fastest growing areas in computing. An extensive increase in the size of the chip and the number of transistors per unit area allows developing chips with tens and hundreds of processor cores even now. At the same time, the problem of effective organization of a communication subsystem that would ensure quick data exchange between nodes of such NoCs is becoming urgent [2]. Since communication is carried out within the framework of a single chip, laws and principles that exist in telecommunication networks work differently in NoCs; therefore, the development of new solutions and methods for organizing data transfer in such networks is required. In particular, it is the topology that has a significant impact on NoC performance. The most common are classical regular topologies such as mesh [3] and torus [4], which do not always meet modern requirements for NoCs, especially with an increase in the number of nodes [5]. Different researchers suggest other topology options among which we can distinguish hypercube [6, 7, 8], chordal ring [9, 10], spidergon [11], L-networks [12]. Their feature is that they can be reduced to presentation in the form of ring-like graphs called circulant topologies in the general form. Optimal circulants have several significant advantages over mesh and torus topologies – better structural survivability, reliability, and connectivity [13, 14].

Software Defined Networking (SDN) has focused enormous attractiveness in changing conventional network by means of offering flexible and dynamic network management. It has drawn important concentration of the researchers from together academia and industries. Mainly, integrating SDN in Wireless Body Area Network (WBAN) applications specifies capable results in terms of handling with the issues like traffic management, security, energy efficiency etc. Recent improvements in miniaturization and energy efficient physiological sensor designs in SDN based Wireless Body Area Networks (WBANs) paved the way for health monitoring systems for collection and processing the real-time physiological data. The collection of signals from different sensor allows reliable diagnosis in heterogeneous than in homogeneous WBANs. Inspired by the evolutions of heterogeneous WBANs, a study on Wireless Electroencephalography Sensor Networks (WESNs) is carried out under distributed signal processing. The distributed WESNs are designed under two different hierarchy i.e. Hierarchical Fully-Connected Topology (HFCT) and Ad-Hoc Nearest-Neighbor Topology (ANNT) to improve the energy-efficiency using distributed Multi-channel Weighted Weiner Filter design (MW2F). Here, each module transmits linear combination of local channels with other modules. The power efficiency is improved Journal Pre-proof in MW2F signal processing algorithm by avoiding centralization of EEG data. A case study is carried out to test the reduced energy consumption after the removal of eye blink artifacts and it is tested with centralized counterparts. The MW2F is evaluated in both topologies against centralized environments and significant reduction of eye blink artifacts improves the energy efficiency in HFCT than other topologies.

Introduction

The energy efficient and miniaturization is the most hot research area in health monitoring systems, where the body is placed with several sensor nodes. These nodes can collect the EEG data and process it in real time and measures are considered to improve the energy efficiency. This system is called as Wireless Body Area Networks (WBANs) [1], and it offers higher demand in the area of physiological monitoring in hospital environment. The WBAN system is composed of several nodes with physiological sensors, wireless communication devices and a signal processing unit. These nodes can communicate with other sensor nodes placed over the body or it can be connected to a fusion center or a storage device. The signals collected from various resources are analyzed jointly in heterogeneous WBANs in real time environment for medical diagnosis.

1- Introduction

2- Wind Energy System

3- WECS Topologies and Generators

4- Power Converters

5- Conclusions and Recommendations

Reference

Abstract

Sources of renewable energy such as wind energy are indigenous and can help in decreasing the reliance on non-renewable energy sources. After introducing the history of wind energy production in Egypt and worldwide besides its techno-economic importance, this paper presents a comparative review on the wind energy conversion systems (WECS). The horizontal and vertical types of wind turbines with their mathematical dynamic models are discussed. Different types of electrical generators used in WECS in addition to their advantages and disadvantages are illustrated. The various AC-AC converters topologies for WECS are explained with a detailed discussion for their features.

Introduction

Worldwide energy demand is rapidly expanding due to the continuous economic progress and the power electronic technologies development. Conversely, traditional petroleum derivatives, for example, coal, oil and flammable gas, which have been the key energy source since the industrial revolution, are confronting rapid consumption. Therefore, the mission to create inexhaustible and clean sources of energy from wind, solar and hydrogen energy becomes more convenient. Among these numerous sustainable assets, wind energy encourages business prospects in substantial power in favor of its safe and naturally sustainable behavior, zero emission, competitive and inexhaustible nature [1, 2]. In 2018, wind energy provides around 5% of the total energy consumption of the world [3]. It is anticipated that energy from wind will be about (20 % and 35%) of the total world’s energy in (2030 and 2050) respectively [4]. The worldwide wind energy production has reached 690.8 GW in 2018 from around 19057 wind farms and Europe had the first place with 259 GW and 13290 wind farms [6]. Figure 1(a) depicts the global wind power production in the last two decades in 2018[5]. Production of wind power for the top five countries across the world in 2018 is illustrated in Figure 1(b). China has the highest wind production in the world with 123.805 GW [5]. The USA provides the highest wind power in Americas and the second in the world with 98.94 GW [7]. Germany is the highest in Europe and the third in the world with 52.828 GW. Africa has the lower wind energy production in the world continents with 5.7 GW with 87 wind farms as depicted in Figure 1(c) [5]. Wind power in South Africa is the highest in Africa with 2295 MW [5]. Egypt has the third place and produces around 810 MW of wind energy and the potential of this production is expected to reach (1500 MW and 2500 MW) in (2030 and 2050) respectively [5]. The electrical power generation from wind energy in Egypt started in 1997 by 6 MW and increased gradually as shown in Figure 1(d) to reach 810 MW in 2018 [5]. In onshore, the blowing wind from the sea to land is usually less than perfect. In addition to, the tendency of waves increases and short period swell is created [8]. Therefore, there is an increased interest for offshore wind, as the breeze is regularly more grounded and more uniform in surface of ocean than aground and the expected generated power from the planned offshore is 143.36 GW [9]. European nations are the pioneers in offshore wind. For around 20 years, Denmark has been using it to supply power. Offshore wind in the USA and similar nations can possibly turn into a noteworthy energy hotspot for household applications as waterfront wind sources are plenty [10].

Based on the concept of switched-capacitor based multilevel inverter topology, a new structure for a boost multilevel inverter topology has been recommended in this paper. The proposed topology uses 11 unidirectional switches with a single switched capacitor unit to synthesize nine-level output voltage waveform. Apart from the twice voltage gain, self-voltage balancing of capacitor voltage without any auxiliary method along with reduced voltage stress has been the main advantages of this topology. The merits of proposed topology have been analyzed through various comparison parameters including component counts, voltage stresses, cost and efficiency with a maximum value of 98.3%, together with the integration of switched capacitors into the topology following recent development. Phase disposition pulse width modulation (PD-PWM) technique and nearest level control PWM (NLC-PWM) have been used for the control of switches. Different simulation and hardware results with different operating conditions are included in the paper to demonstrate the performance of the proposed topology.

Introduction

With the rapid growth of the renewable energy resources and its application in high voltage applications link industrial drive, high voltage dc transmission (HVDC), electric vehicle (EV), etc., power electronic converters play an important role in the power conversion suitable for each application. Multilevel inverters have its own importance in medium and high voltage applications due to reduced voltage rating of power semiconductor devices for high voltage generation, reduced harmonic contents, the small size of the filter, reduced EMI, improved efficiency and many more. Neutral point clamped (NPC), flying capacitor (FC) and cascade H-bridge (CHB) are traditional topologies that have been extensively researched and applied in different applications. However, the major concerns with these topologies have been the higher number of components, capacitor voltage balancing and complex control for a higher number of levels [1]–[4]. Therefore, numerous topologies have been proposed with reduce switch count [5]–[14]. In [7], an optimal design of multilevel inverter topology has been discussed. The topology of [7] uses several isolated dc voltage sources which restricts its applications. Another topology based on isolated voltage sources has been proposed in [9], in which 17 level output voltage has been achieved using four voltage sources. Similar topologies have been proposed in [10], [12]. In these topologies, higher number of levels can be generated to improve the quality of the output voltage. However, most of these topologies lack the voltage boosting ability.

As Internet of Things (IoT) develops, IoT technologies are starting to integrate intelligent cameras for managing safety within mental health hospital wards and relevant spaces, seeking out specified individuals from these surveillance videos filmed by the various cameras. Because monitoring is one of the important application of IoT based on distributed video cameras. In order to fine-grained re-identification of patients and their activities against the very low resolution, occlusions and pose, viewpoint and illumination changes, we propose a novel data-driven model to infer multi-cameras logical topology and re-identify patients captured by different cameras. In our model, we employ a Time-Delayed Mutual Information (TDMI) model in order to address multi-cameras logical topology inference. Additionally, we use a welltrained Deep Convolutional Neural Network (DCNN) to extract characteristics. Moreover, we employ a name-ability model to discover deep attributes and a classifier based on a structural output of attributes is designed to tackle the re-identification of patients, especially who possess psychiatric behaviour. In order to improve the present model’s performance, we resort to the parallelized implementations. Experimental results show that our model possesses the best performance as compared to state-of-the-art model,especially, when the semantic restrictions are imposed onto the production of patients’ specific attributes with structural output. Further, the deep learning model is used to produce characteristics when there is no supervision on the learning model of attributes.

Introduction

From last two decades, videos surveillance systems have been used for managing safety within healthcare industry especially in mental health hospital, asylum, seclusion room, and wards for controlling the suspicious activities of psychotic patients. Such as spotting suspicious behavior, violence, hyperactivity, and safety issues. Further, these issues lead to treatment-related harms caused by medical care of patient and staff. Additionally, those adverse events also lead to harm caused by errors, medical treatment errors, potential for harm. further, multi-cameras monitoring surveillance system can sort the problems related to managing medical care protocols [1]–[3]. Health monitoring research leads to significant focus to improve and achieve good healthcare environment. The analytical evaluation of mentioned issues and analyzing their causes can help in healthcare planning and design advance smart health care system too. Unfortunately, most hospitals and health systems have rudimentary methods to identify this issues or activities. These rudimentary methods mostly depend on the use of voluntary help, chart-reports results, and scanning of manually observed behavior of patient. These methods face challenges and limitations those lead them to accidents. Further, these methods required human resources as well. Detecting these events is especially challenging because of the unpredictable nature of patients. Along with powerful re-identification features, this paper proposes a model to improve patient safety in hospitals through the usage of Multi-Cameras IoT.

Piecewise linear functions have been used to model and solve non-linear problems in science, social science, and technology fields since the 1950‟s. Multi-choice goal programming (MCGP) has been widely used to solve multiple objective decision-making (MODM) problems in which each goal mapping with multiple aspiration levels is allowed, expanding the original feasible region to obtain better solutions in the MODM problems. This paper integrates the efficient S-shaped penalty method, arbitrary piecewise linear utility functions, trapezoidal utility functions, and MCGP to solve a topology design problem in a remote patient monitoring system (RPMS) by providing universal senior citizen coverage in which quantitative (e.g., patient satisfaction-related cost) and qualitative (e.g., satisfaction) issues are considered simultaneously. In addition, some novel utility functions such as the force utility function, indicator utility function, and arbitrary utility function are proposed to improve the usefulness of MCGP in the field of management science. These proposed utility functions can be easily used to model qualitative issues in real-world problems. A topology design problem for an RPMS is demonstrated to justify the feasibility, usefulness, and compatibility of the proposed methods. Further, sensitivity analysis and managerial implications are provided using an RPMS in Taiwan.

Introduction

Given the aging society and falling birth rate, Taiwan has been classified as an “aged” society since 2018 and will turn into a “hyper-aged” society by 2025. The increasing healthcare costs for the growing population of elderly, chronic diseases, as well as the demand for new technologies and treatments in addition to the decrease in the number of healthcare professionals compared to the elderly increase make the traditional healthcare service model inadequate. Conventional healthcare model inadequacy can be explained by the following two aspects: (1) large hospitals (e.g., accredited hospitals and medical centers) are always overcrowded, and as a result, people often don‟t have access to outpatient clinics and must wait a long time for inpatient care; and (2) increasing healthcare costs also place a considerable burden on the government‟s finances. Nowadays, establishing an efficient remote patient monitoring system (RPMS) to take care of the greater number of elderly people as well as reduce healthcare costs is essential in Taiwan. Moreover, it is very important to build an RPMS with a more complete topology network under a limited budget to take care of senior citizens. To implement the RPMS for serving senior citizens, several goals and criteria should be considered simultaneously. These goals and criteria contain both quantitative (e.g., the RPMS setup cost and investment cost) and qualitative (e.g., satisfaction of senior citizens) issues, and the balance between them (e.g., balancing the setup cost and the satisfaction of senior citizens), complicates the problem. This is a typical multiple objective decision making (MODM) problem with conflicting goals.

RUMORS spreading in social networks have long been a critical threat to our society. A recent incident of rumors “Obama was injured in two explosions of White House” led to 10 billion USD losses in a few hours. This demonstrates that a single rumor can cause great damage to business and life [1]. Nowadays, with the development of mobile devices and wireless techniques, the temporal nature of social networks (time-varying social networks) has a deep influence on dynamical information spreading processes occurring on top of them [2]. The ubiquity and easy access of social networks not only promote the efficiency of information sharing but also dramatically accelerate the speed of rumor spreading [3]. Rumors combine the characteristics of the “word-of-mouth” spreading scheme with the dynamic connections between individuals in time-varying social networks [4]. For either forensic or defensive purposes, it has always been a significant work to identify the source of rumors in time-varying social networks [5]. However, the existing techniques generally require firm connections between individuals (i.e., static networks) so that administrators can trace back along the determined connections to reach the spreading sources. For example, many methods rely on identifying spanning trees in networks [6]–[8], then the roots of the spanning trees are regarded as the rumor sources. The firm connections between users are the premise of constructing spanning trees in these methods. Some other methods detect rumor sources by measuring node centralities [9], [10]. The individual who has the maximum centrality value is considered as the rumor source. All of these centrality measures are based on static networks. Time-varying social networks, where users and interactions evolve over time, have led to great challenges to the traditional rumor source identification techniques.

Over the past decades, supply chains have grown longer and became interconnected as a result of globalisation and rising cost pressures (Christopher and Holweg, 2011). Interconnectedness implies that a failure in one supply chain entity can potentially cascade across the whole network (Schmitt and Singh, 2012), making risk monitoring and mitigation challenging. Suppliers of multiple tiers are tied together creating emergent, yet predictable connection patterns, described as “supply network topology” (Thadakamalla et al., 2004). Studies on network topology, conducted under the framework of network science aim to unveil the behavioural phenomena of interconnected systems, which cannot be well understood from the perspective of a single entity. Understanding how the decision-making of multiple interconnected entities influence overall network resilience is necessary to cope with disruptions effectively because failures are more likely to propagate in certain topologies (Watts, 2002). Supply Chain Risk Management (SCRM) methods rarely consider the impact of disruptions on the extended supply network, where the term extended refers to ties beyond a firm’s direct suppliers and customers. The relationship between supply network topology and the effectiveness of recovery from disruptions using risk management strategies has not yet been explored. We aim to address this gap as follows. After reviewing previous work done in the field of SCRM and complex supply networks, we employ a modelling approach, where several theoretical network topologies based on the extant empirical literature are generated. The generated topologies are used to configure a supply network, after which the networks are subjected to random disruptions. Two SCRM strategies, namely inventory mitigation and contingent rerouting, are applied and the extent to which these strategies are able to enhance network recovery is observed. Our results sound a cautionary note. We find that the effectiveness of the two SCRM strategies is moderated by the topology of the supply network and that an increased understanding of supply network topology is necessary to underpin the choice of an effective strategy. First, it is shown that inventory mitigation outperforms contingent rerouting in a complex supply network setting regardless of topology. A key lesson is that random topologies need significantly higher inventory levels to recover from disruptions than scale-free networks. It is also observed that contingent rerouting is not effective for scale-free networks due to low numbers of alternative suppliers, particularly for short-term disruptions. We then explore targeted risk management, where only suppliers which suffered the most from disruptions apply a risk management strategy. Targeting suppliers does not always result in cost reduction. On the contrary, targeted inventory mitigation might significantly increase costs when the network is exposed to rare disruptions due to excessive inventory being kept for long periods of time. Targeted contingent rerouting creates inventory oscillations when network is exposed to short-lasting disruptions, resulting in decreased fill-rates and increased costs. Our work motivates further studies on the relationship between the functionality and performance of supply networks and their topology.

INTRODUCTION

Internet of Things (IoT) systems consist of multiple compute platforms, notably edge devices and servers. Within IoT systems, most of the focus has been on the development and integration of novel new edge devices. For example, the recent proliferation of wearable devices designed to monitor personal health has increased significantly in recent years yielding unprecedented awareness of fitness. Similarly, new smart buildings are integrating new sensors with building control systems to improve energy efficiency, occupant comfort, and safety [1], [2]. The raw data obtained using IoT devices provides tremendous operational insight, which is driving the deployment of additional IoT devices. For deployed IoT devices, once sensor values are read the data generated is transmitted across the network and stored on server platforms for later analysis [3]. Once stored, this data is then analyzed, leveraging recent advances in machine learning. To date, most of these IoT analytics have been performed offline, using batch-oriented techniques. However, as IoT analytics transition to online, real-time pipelines that immediately translate raw data into actionable information, earlier approaches to manage streaming data becomes challenging. Additionally, as the number of deployed IoT devices increases, how IoT data is routed and processed must be handled judiciously to prevent overloading and ensure scalability. The server entry point for IoT data generated by edge devices are often message queues. Effectively managing these ingress queues is key to efficiently route streaming data to analytical pipelines while maximizing scalability. One common approach is to use message brokers, such as MQTT or Apache Kafka to establish a single ingress queue. This simplifies the configuration of IoT devices because all data can be sent to the same destination message queue. However, as an IoT deployment scales from a few edge devices to thousands, this single message queue will quickly become a bottleneck. The additional latency caused by using a single queue will then have a ripple effect impacting all downstream analytical processing pipelines. In the extreme this additional latency can cause poor predictions and analysis. In this paper, we describe and characterize the impact of alternative IoT data processing topologies in the context of real-time machine processing pipelines. Our goal is twofold. First, we identify and compare the performance impact of using different topologies that leverage different levels of queue-level parallelism for server-side processing of IoT data. Second, we identify patterns using real deployment data (e.g. smart buildings) that can be generalized for diverse IoT deployments. We leverage the complete realtime streaming platform that we developed in previous work [4] and extend this system to use alternative topologies that increase queue parallelism. We use real building sensor data collected by the facilities department at the University of Washington for a LEED certified building that was recently renovated. These data streams, sourced from multiple installed sensors are then routed through our real-time streaming system to our analytical pipelines to gain insights into room occupancy within that building. We then analyze the performance impact of several alternative topologies.

1- Introduction

2- Related works

3- MSPSO

4- Experimental verification and comparisons

5- Conclusion

References

Abstract

This paper proposes a multi-swarm particle swarm optimization (MSPSO) that consists of three novel strategies to balance the exploration and exploitation abilities. The new proposed MSPSO in this work is based on multiple swarms framework cooperating with the dynamic sub-swarm number strategy (DNS), sub-swarm regrouping strategy (SRS), and purposeful detecting strategy (PDS). Firstly, the DNS divides the entire population into many sub-swarms in the early stage and periodically reduces the number of sub-swarms (i.e., increase the size of each sub-swarm) along with the evolutionary process. This is helpful for balancing the exploration ability early and the exploitation ability late, respectively. Secondly, in each DNS period with special number of sub-swarms, the SRS is to regroup these sub-swarms based on the stagnancy information of the global best position. This is helpful for diffusing and sharing the search information among different sub-swarms to enhance the exploitation ability. Thirdly, the PDS is relying on some historical information of the search process to detect whether the population has been trapped into a potential local optimum, so as to help the population jump out of the current local optimum for better exploration ability. The comparisons among MSPSO and other 13 peer algorithms on the CEC2013 test suite and 4 real applications suggest that MSPSO is a very reliable and highly competitive optimization algorithm for solving different types of functions. Furthermore, the extensive experimental results illustrate the effectiveness and efficiency of the three proposed strategies used in MSPSO.

Introduction

Particle swarm optimization algorithm (PSO) is a widely known evolutionary algorithm proposed by Kennedy and Eberhart in 1995 [1, 2]. During the optimization process, each particle adjusts its flight direction and step-size re5 lying on the information extracted from the past experience of itself and its neighbors. Although the search pattern of each particle is quite simple, the search behavior of the entire population is very complex, and the population shows great intelligence owing to the cooperative behavior among particles. Due to the simplicity of implementation, PSO has been applied for many academic 10 and real-world applications [3, 4, 5]. Extensive studies reveal that PSO’s performance mainly depends upon its two characteristics [6, 7]: exploration and exploitation. However, there is a contradiction between the two capabilities. In order to be successful, PSO needs to establish a good ratio between exploration and exploitation. A common 15 belief is that PSO should start with exploration and then gradually change into exploitation. Hence, many time-varying strategies are proposed to regulate the parameters and neighbor topology involved in PSO. For example, in the most ubiquitous update rules of parameters introduced in [8, 9], three parameters involved in PSO are adjusted based on the iteration 20 numbers aiming to meet different search requirements of different evolutionary stages. The fundament thought of these modifications is tuning particles’ learning weights for their exemplars.

Agriculture and energy are closely related since efficient use of energy is a key factor in sustainable agricultural production. Increasing requirement of higher food production has led to intensive use of agricultural and natural resources (Khoshroo, 2014). However, bio-energy has placed agriculture in the position of energy consumer and energy supplier (Esengun et al., 2007). Efficient energy use in agriculture is a pathway toward decreasing environmental hazards and improving agricultural sustainability (Izadikhah and Khoshroo, 2018). Energy demand in agriculture can be classified into direct and indirect energies or renewable and non-renewable energies (Ozkan et al., 2004a). Direct energy consists of human labor, diesel fuel, electricity and water for irrigation, while farmyard manure (FYM), chemicals and machinery are considered indirect energy. Renewable energy includes human labor, FYM and water for irrigation whereas machinery, diesel fuel and chemicals are considered non-renewable forms of energy (Demircan et al., 2006; Ozkan et al., 2004a). The established method to determine energy efficiency of production systems is the input-output analysis. Using this type of analysis, researchers have studied energy consumption in the production of fruits such as citrus (Ozkan et al., 2004), grape (Ozkan et al., 2007), apple (Gokdogan and Baran, 2017; Taghavifar and Mardani, 2015), prune (Tabatabaie et al., 2013), walnut (Khoshroo and Mulwa, 2014) and pomegranate (Houshyar et al., 2017). Modeling crop yield based on energy consumption is an interesting issue for researchers. Prediction of agricultural production is useful for farmers, governments, and agribusiness industries. It helps farmers to make marketing decision. Government requires forecasts of the crop yield to implement policies that provide technical and market support for the agricultural sector. Processors of food, and others in the marketing chain, need forecasts for their purchasing and storing decisions. Various approaches and methods have been used to model energy consumption (Arabi et al., 2017; Jebaraj and Iniyan, 2006; Laha and Chakraborty, 2017; Say and Yücel, 2006; Tso and Yau, 2007). Traditionally, econometric models, based on Cobb-Douglass production function were the most popular modeling technique for investigating functional relations between input energy and various crop yield (Hamedani et al., 2011; Hatirli et al., 2006; Houshyar et al., 2015).