Abstract

Keywords

References

Abstract

In this paper, the traditional ant colony algorithm has a slow convergence rate for routing optimization of Mesh networks. A multi-path routing protocol based on improved ant colony algorithm, Fortified Ant protocol, is proposed. The protocol first adds a sorting algorithm based on the ant colony algorithm, and introduces the concept of elite ants to improve the speed of routing optimization. Secondly, this paper also studies the multipath transmission of self-organizing networks. The simulation results show that compared with ADOV, DSR and AOC routing algorithms, the algorithm can quickly find multiple paths with better quality, with fast convergence and overhead. Less advantage.

1. Introduction

Wireless Mesh network is a kind of self-organizing network, mainly composed of two parts, respectively is wireless router (Mesh routers) and wireless client (Mesh clients), Mesh router constitute the backbone network, Mesh clients through the Internet backbone network connection.Under the communication environment of wireless mesh network, the topology of mesh network is relatively stable, so it has great research value in the temporary arrangement of communication network.

As the future of wireless Mesh networks has gradually been recognized by many scholars, many experts have studied the multipath routing methods of wireless Mesh networks, and many have achieved certain results. In [1], a multi-path routing discovery method based on particle swarm optimization for wireless mesh networks is proposed. The congestion prediction degree and node forwarding goodness function are defined as the fitness function of the particle swarm algorithm model to ensure the validity of the calculated path. High efficiency. Literature [2] mainly studies the feasibility of ant colony algorithm applied to route optimization in Mesh networks. Simulation experiments show that ant colony algorithm can be used for route optimization of Mesh networks, and its optimization speed is fast, and it can achieve global optimization.

In this paper, on the basis of above research, in view of the traditional Ant colony algorithm optimization is slow and single path routing is easy to lose data grouping problem, put forward a kind of Ant colony algorithm based on sorting multipath routing protocols – Fortified Ant protocol.

1- Motivation and significance

2- Software description

3- Illustrative example

4- Impact

5- Conclusion

References

Abstract

Ant Colony Optimisation (ACO) algorithms emulate the foraging behaviour of ants to solve optimisation problems. They have proven effective in both academic and industrial settings. ACO algorithms share many features among them. Isula encapsulates these commonalities and exposes them for reuse in the form of a Java library. In this paper, we use the travelling salesman problem and image segmentation to showcase the framework capabilities using three top-performing ACO algorithms implemented in Isula. This framework is an open-source project available at GitHub, where is currently the most popular ACO java repository.

Motivation and significance

Ant Colony Optimisation (ACO) algorithms, proposed by Dorigo et al. [1], solve optimisation problems by emulating the behaviour of ants in nature. When ants traverse a territory searching for food, they mark their path with pheromone. After they have located food, they make several trips from the food source to the nest, increasing the intensity of their path’s pheromone trail. In case several ants have located the same food source and are also transporting food to the nest, the pheromone trail of the ant with the shortest path is more intense. The shortest path, in the same amount of time, has more two-way trips than longer paths that require more time for their traversal. Fellow ants are sensitive to pheromone and tend to select the path with the most intense pheromone trail. This behaviour also increases the pheromone intensity of the shortest path. Over time, the whole colony converges towards the optimal solution.

This article addresses a Periodic Vehicle Routing Problem with Time Window and Service Choice problem. This problem is basically a combination of existing Periodic Vehicle Routing Problem with Time Window and Periodic Vehicle Routing Problem with Service Choice. We model it as a multi objective problem. To solve this problem, we develop a heuristic algorithm based on Improved Ant Colony Optimization (IACO) and Simulate Annealing (SA) called Multi Objective Simulate Annealing – Ant Colony Optimization (MOSA-ACO). Improvements are made in following respects: a) a Euclidean distance based solution acceptance criterion is developed; b) a parameter control pattern is designed to generate different initial solutions; c) several local search strategies are added. Benchmark instances generated from Solomon’s benchmark instances and Cordeau’s benchmarks instances are applied. Comparison algorithms include four population based heuristics and IACO. Computation experiment results show that MOSA-ACO algorithm has a good performance on solving this problem.

Introduction

In this article, we propose a special variant of existing Periodic Vehicle Routing Problem (PVRP) called Periodic Vehicle Routing Problem with Time Window and Service Choice (PVRPTW-SC). We give out a problem model which is an extension of PVRP-SC problem model. Then, a hybrid heuristic algorithm called Multi-Objective Simulation Annealing – Ant Colony Optimization (MOSA-ACO) is applied to this problem. Finally, computation experiment results are reported. Experiment instances include instances generated from VPRTW benchmark instances and PVRPTW benchmark instances. Experiment results show that the MOSA-ACO has a good performance on PVRPTW-SC. This research is first inspired by Jiting et al. [1]. In this paper, a geostationary orbit (GEO) satellite observation planning problem is introduced. The GEO satellite observation planning problem includes a GEO satellite and several targets on earth to be visited. The aim of GEO satellite observation planning problem is to find an observation plan with lowest cost and highest profit. To solve this problem, a NSGA II based algorithm and a Multi Objective Travelling Salesman Problem (MOTSP) model are implemented. In this paper, we extend this problem in several directions: a) in PVRPTW-SC problem, multi trips in a planning horizon are considered; b) time window constraint is considered; c) a changeable visit frequency of each customer in planning horizon is considered.

1- INTRODUCTION

2- THE BASIC CONCEPTS OF IMPROVED ANT COLONY OPTIMIZATION

3- THE BASIC CONCEPTS AND FRAMEWORK OF INTERVAL TYPE-2 TSK TYPE FUZZY LOGIC SYSTEM

4- THE DESIGN OF A2-C1 TYPE INTERVAL TSK FUZZY LOGIC SYSTEM BASED ON IACO

5- APPLICATION EXAMPLES

6- CONCLUSION

REFERENCES

Abstract

An Improved Ant Colony Optimization (IACO) is proposed to design A2-C1 type fuzzy logic system (FLS) in the paper. The design includes parameters adjustment and rules selection, and the performance of the intelligent fuzzy system, which can be improved by choosing the most optimal parameters and reducing the redundant rules. In order to verify the feasibility of the proposed algorithm, the intelligence fuzzy logic systems based on the algorithms are applied to predict the Mackey-Glass chaos time series. The simulations show that both the IACO and ACO have better tracking performances. The results compared with classical algorithm BP ( back-propagation design) shows the tracking performance of IACO is more precise, the result compared with ACO shows that either the training result or the testing result, the tracking performance of IACO is better, and IACO has a faster convergence rate than ACO, the results compared with the Intelligent type-1 fuzzy logic systems show that both the A2-C1 type FLS based on IACO and ACO have better tracking performance than type-1 fuzzy logic system.

INTRODUCTION

Nowadays, fuzzy logic system is applied into a variety of fields. Professor L. A. Zadeh proposed the conception of fuzzy set first time [1], from that time on, fuzzy logic system attracts a large number of scholars to research. Although, fuzzy logic systems have some advantages, but there are several shortcomings, the main shortcomings are that it is too difficult to obtain the optimal parameters and rule explosion. Recent years, it has been a research hot spot that adopting intelligent algorithms to design of fuzzy logic system, respectively. Lian et al. optimized the radial basis function neural network based on QPSO, and the result was better than BP algorithm and least square method [2]. Zhai et al. combined the QPSO with the non single point interval type-2 fuzzy logic system and used to design of image noise filter, the result was better than traditional non fuzzy methods [3]. Yazdi combined GA with fuzzy logic system, and it was applied to the optimization of off center braced frame system, compared with the structure optimized only by GA, the effect was more remarkable [4]. Juang et al. combined ant colony optimization with fuzzy cluster to design fuzzy logic system, and used for nonlinear plant tracking control, compared with PSO and GA, the results were more better [5].

I- Introduction

II- Related Work

III- The ACO Algorithm

IV- A New Multi-Population Co-Evolution Ant Colony Optimization(ICMPACO) Algorithm

V- Application of the ICMPACO Algorithm for Solving TSP

VI- APPLICATION OF THE ICMPACO ALGORITHM FOR SOLVING GATE ASSIGNMENT PROBLEM

VII- CONCLUSION AND FUTURE WORK

References

Abstract

In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

INTRODUCTION

Ant colony optimization (ACO) algorithm was proposed by Dorigo in 1992 [1]. It is a heuristic evolutionary algorithm based on population, which is inspired by the research results of the collective behavior of the real ants in nature. It has been proved that the ACO algorithm takes on a better optimization performance in solving optimization problems. The ACO algorithm relies on the activities of many individualities and feedback of information. Although the activity of ant is very simple, the activity of whole ant colony is acceptable. The ACO algorithm has the characteristics of distributed computing, positive feedback and heuristic search. In essence, it is a heuristic global optimization algorithm in the evolutionary algorithm [2]–[11]. In process of the evolution, the information interaction based on pheromone plays a very important role. Due to the advantages of the ACO algorithm, it is widely applied in solving combinatorial optimization problems, such as the traveling salesman problem, assignment problem, job-shop scheduling problem, vehicle routing problem, graph coloring problem and network routing problem and so on [12]–[25]. A lot of experts have devoted themselves to the research of the ACO algorithm, and some improved ACO algorithms are proposed to solve the complex optimization problems.

1- Introduction

2- Literature review

3- STEP-NC enabled approach

4- Energy consumption calculation via STEP-NC

5- Proposed model by considering energy consumption

6- An improved ACO solution for the proposed model

7- Case study and discussions

8- Conclusions

References

Abstract

Sustainable production plays an important role in product lifecycle management by considering the social sustainability. Energy-efficient machining is an efficient approach for sustainable production in current manufacturing sectors. Although many related efforts have been achieved, a comprehensive energy optimization approach oriented to manufacturing parts is still a challenge. Therefore, this paper selects Standard for the Exchange of Product model data-Numerical Control (STEP-NC) as the enabling technology to achieve energy-efficient machining. An optimization model is proposed based on the energy calculation method using the workingstep in STEP-NC. An improved ant colony optimization (ACO) solution, consisting of encoding and decoding, initialization, machining scheme generation, idea of local multiple iteration, evaluation, pheromone evaporation and update, is presented. A part with typical manufacturing features is applied to verify the effectiveness of the proposed approach. The generated solution can provide a comprehensive machining scheme for low energy demandI by improving the efficiency with 25% for solving the optimization problem.

Introduction

Due to soaring energy prices and environmental pollution, research on sustainable product lifecycle management (SPLM) has been focused recently. Sustainable production is one important phase of SPLM. Reducing energy consumption during machining operations plays a critical role in achieving sustainable production, i.e., energy-efficient machining. Energy-efficient machining has attracted increasingly more efforts in recent years (CamposecoNegrete, 2013; Gong et al., 2016; Velchev et al., 2014; Yan and Li, 2013; Zhou et al., 2016). Deciding machining schemes (MSs) for a part to be machined from the perspective of energy efficiency is an effective way to perform energy-efficient machining. A MS consists of many key elements, e.g., machining resources, machining parameters, tool path, process route, etc. In general, there exist more than one reasonable MS, which could constitute a group of candidate machining schemes (CMSs), and the best machining scheme (BMS), i.e., energy-efficient machining scheme (EEMS), is generated from this group. It is obvious that this process refers to an optimization problem. To solve the optimization problem, two key procedures are necessary, i.e., optimization model and the corresponding solution method. An optimization model includes optimization objective, optimization variables and constraints. The objective may be single (i.e. only energy consumption) or multiple (e.g. time, energy, tool life, etc.). No matter single-objective or multipleobjectives optimization, an energy consumption model for indicating the energy calculation approach during machining processes is essential, which is used to formulating the optimization objective. Besides, the optimization variables are the contributing factors to the objective, and the constraints are determined in terms of the specific conditions. Regarding the solution method, a metaheuristic algorithm is a type of effective method of solving the optimization model, such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), tabu search (TS), simulated annealing (SA) and NSGA-II.

The use of enhanced clustering techniques and multiple mobile sinks can improve the data-collection efficiency and reduce the energy consumption of wireless sensor networks. Most of the existing approaches use static sinks and multihop routing, which can cause data loss and the early death of sensor nodes, leading to energy-hole problems and inefficient data collection. Nowadays, sensors produce time-sensitive data, and hence, lossy data-collection approaches must be avoided. In order to overcome the aforementioned issues and improve the network lifetime, we propose an enhanced clustering methodology with multiple mobile sinks for efficient data collection. The performance of the proposed method is tested and compared with that of the existing algorithms LEACH, GA, and PSO. The simulation results show that the proposed approach improves the network lifetime significantly and reduces data loss.

Introduction

Sensors are small electronic devices used to monitor or measure changes in real-time environments [1]. They are usually inexpensive and low-power-consuming devices, which are capable of processing and transferring data to a base station. After successful deployment, the sensor nodes form a network, by selforganization, which is called the wireless sensor network (WSN). The primary work of a sensor node is to log the analog information of environmental changes and convert it into digital information with the help of analog-to-digital converters. In addition to logging information, the sensor nodes also act as repeaters to forward the data from other sensor nodes to the base station through single or multihop communication. Generally, the nodes are operated with limited battery resources, and the process of recharging or replacing the battery after deployment is tedious. Thus, the limited lifetime of battery is one of the fundamental issues in WSNs [2–4]. Sensor technology has various applications, which include industries, automated homes, military applications, modern health care, environmental monitoring, and security, to name a few [5–9]. In WSNs, information from the sensor nodes is usually transferred to the sink through multihop communication, in order to reduce the energy consumption of the nodes. Though such a data- ∗ Corresponding author. E-mail address: yoonmojung@skku.edu (Y.M. Jung). forwarding approach improves the overall lifetime of the network, it also causes energy-hole problems and the premature death of sensor nodes [10,11]. In particular, with this approach, the sensor nodes close to the sink forward not only their own data to the sink but also the data from their neighboring nodes.

Abstract

Realizing safety improvements in construction site layout planning (CSLP) is vitally important to construction project safety management. Unlike previous studies in which the safety objective is built without detailed risk factors analysis, this study transforms CSLP into a multi-objective optimization (MOO) problem with designing two safety objective functions due to facility safety relationships (potential risks arising from interaction flows) and geographic safety relationship (potential risks arising from hazardous sources) from the holistic interpretation of interaction relationship connecting temporary facilities. Besides, a supplementary cost reduction objective function was also derived as cost is a critical barrier against safety improvement. Subsequently, a triobjective ant colony optimization based model was developed to solve MOO problem. Finally, a case study is used to verify the proposed model. The study enriches safety implications by considering onsite safety issues from interaction relationship and enhances site safety of CSLP in the pre-construction stage.

Introduction

Construction site layout planning (CSLP) is a critical activity that should, ideally, be given full consideration to early in the pre-construction stage of construction projects. Various researchers have discussed the significance of “design for safety” and argued that most accidents or serious risks can be avoided by incorporating more safety considerations into planning schemes [1–4]. It is significantly beneficial and vital to improve construction site safety through better site layout in safety planning, and thus the considerable attention paid to safety planning in the pre-construction stage is critical to improving project safety performance efficiently [5].

CSLP is commonly treated as an optimization problem, and site safety can be realized by designing different objective functions based on safety requirements or considerations (hereafter called “safety objective function”), such as minimizing accidents by optimizing safe locations for tower cranes [6–8], controlling hazardous materials onsite [9, 10], reducing intersections between heavily traveled routes [11], defining the safety zones in term of necessary regulations [12], and reducing the noise pollution onsite [13, 14]. With a specific safety objective function, the generated site layout plan can only fulfil the partial safety requirement mentioned above. In previous researches, limited attention has been paid to designing safety objective functions with full risk factor evaluation involved in the function design. Developing safe construction site layout plans under the partial safety objective function without adequate and further risk factors analysis will result in layout with more risk tendency. Thus, this study was conducted with the objective of designing safety objective functions with full consideration of onsite safety after holistic risk factors analysis associated with site layout.

Due to the limited energy of the sensor nodes, the unreasonable clustering routing algorithm will cause node premature death and low utilization of energy efficiency in wireless sensor network (WSN). In Adaptive Thresholdsensitive Energy Efficient Network (APTEEN), the assignments of the cluster head (CH) are much heavier than other nodes. The CH unbalanced energy dissipation between nodes that make them die prematurely. Ant colony algorithm can avoid this problem, so this paper presents a double cluster heads Adaptive Threshold-sensitive Energy Efficient Network based on ant colony (ADCAPTEEN). ADCAPTEEN optimizes the cluster head election method compared with APTEEN. It suggests that one master cluster head (MCH) and one vice cluster head (VCH) will be selected in each cluster. The double cluster heads (DCH) can co-work on data collection, fusion, transition, etc. To make routes more stable and energy efficient, this paper proposes a Multiple Adaptive Threshold-sensitive Energy Efficient Network based on Ant-colony (AMAPTEEN). It is the optimization of ADCAPTEEN. And CH selects intermediate node (IM_node) multiple times with ant colony algorithm per round in each cluster, and this way forms multiple route transmission data. Simulation in OPNET proves that compared with APTEEN, ADCAPTEEN reduces energy dissipation, improves node survival rate, and extends network life cycle. AMAPTEEN delays the time of node death, balances energy consumption, and extends network lifetime further operating in the same settings compared with ADCAPTEEN. The proposed two algorithms have good scalability, and they are suitable for large-scale network.

Introduction

Wireless sensor networks (WSN) consisting of a large number of sensor nodes are deployed randomly in monitor areas. The applications of the WSN have grown rapidly over the recent years. The performance and the quality of WSN are closely related to the type of routing protocol. Clustering routing protocol commonly used as clustering protocol can effectively facilitates saving energy [1]. Basing on the mode of functioning, WSNs can be classified into proactive, reactive, and hybrid networks [2]. Under proactive network application mode, sensor nodes continuously monitor the surrounding environment and send periodically the monitored data to the user at a constant cycle rate. The reactive network, however, only transmits data when the observed variable changes, and the change reaches the conditions that need to be sent. The Low-Energy Adaptive Clustering Hierarchy (LEACH) [3] protocol, one of the early cluster algorithms, belongs to proactive network, but is not suitable for reactive network. The energy-efficient LEACH (EE-LEACH) protocol for data gathering is introduced [4]. It reduces endto-end delay and energy consumption. But nodes which are selected to forward the data to sink node only consider the nodes’ residual energy. The improved LEACH selects energy-efficient CH which considers the energy and distance [5]. The sum of alive nodes is quite improved. The novel energy-efficient clustering mechanism, based on artificial bee colony algorithm, is presented to prolong the network lifetime [6]. Threshold-sensitive Energy Efficient Network (TEEN) belongs to the reactive mode [7]. The proposed Adaptive Threshold-sensitive Energy Efficient Network (APTEEN) [8] is classified as a hybrid network and preserves the advantages of both proactive network and reactive network. APTEEN protocol can collect data in a circular way and respond to emergencies. Unfortunately, APTEEN exposes its defects—direct communication between CH and sink node, which results in high energy consumption and low efficiency. A proposed Hierarchal Multipath Adaptive Threshold-sensitive Energy Efficient Network Protocol (HMAPTEEN) [9] uses selected nodes in each level for routing to the next level.

Abstract

Vehicles present on the Internet of Vehicles (IoV) can communicate with each other in order to determine the status of the road and vehicle in real time. These parameters are used to estimate the average speed and identify the optimal route to reach the destination. However, the government traffic departments are unable to use these valuable traffic data and thus more traffic jam, congestion and road accident occurs. In order to overcome this issue, this paper proposes an effective traffic control system with the help of IoV technology. The proposed method is demonstrated in the study are of Vellore district, Tamil Nadu, India. The street maps are segmented into number small number of distinct maps. Ant colony algorithm is applied to each map in order to find the optimal route. In addition, Fuzzy logic based traffic intensity calculation function is proposed in this paper to model the heavy traffic. The proposed IoV based route selection method is compared with the existing shortest path selection algorithms such as Dijikstra algorithm, Kruskal’s algorithm and Prim’s algorithm. The experimental results proved the good performance of the proposed IoV based route selection method.

Introduction

Internet of Vehicles (IoV) paradigm originally developed with the help of Internet of Things (IoT) technology. IoV is widely used in various environments it includes communications, transportation, information and automotive technology. The Internet of Vehicles (IoV) is an expected conjunction of the Internet of Things and the mobile Internet. The IoV comprised of all current and new vehicles, either integrated or fitted with two-way RF equipment [51,52]. The IoV uses Vehicle-to-Vehicle (V2V), Vehicle-to-Road (V2R), Vehicleto-Human (V2H) and Vehicle-to-Sensor (V2S) communication networks to communicate between public networks and vehicles. It is predicted that more than 80% of vehicles sold globally in 2020 will be connected, either by smart-phone or embedded device integration [53]. IoV is one of the possible technologies to improve road safety, and provide in-car entertainment and on-board Internet services, such as, social networking, online media streaming, online navigation and real-time traffic report acquisition. Nowadays, a range of computing technologies such as “5G”, “cloud computing” and “big data” have been used to monitor the vehicles, road traffic analysis, accident identification system [54,55]. Moreover, the convergence of IoV technology encompasses environmental protection, information communications, energy conservation, and safety. Due to the rapid growth in connected vehicles, many research issues need to be addressed it includes security, privacy and trust, network resource optimization and QoS guarantee for IoV [56].

Selection of an optimal path is a challenging issue in Mobile Ad-hoc network (MANET). This paper proposes a new mechanism for route selection combining Ad-hoc On-Demand Distance Vector (AODV) protocol with Ant Colony Optimization (ACO) to improve Quality of Service (QoS) in MANET. Based on the mechanism of ant colony with AODV, the finest route for data delivery is selected using pheromone value of the path. In the proposed work, pheromone value of a route is calculated based on end to end reliability of the path, congestion, number of hops and residual energy of the nodes along the path. The path which has highest pheromone value will be selected for transmission of the data packet. The simulation result shows that the proposed scheme outperforms AODV, Dynamic Source Routing (DSR) and Enhanced-Ant-DSR routing algorithms.

INTRODUCTION

Ad-hoc networks are collection of mobile nodes communicating with each other using wireless media without any fixed infrastructure. Mobile ad-hoc network (MANET) (Pahlavan and Krishnamurthy, 2011; Giordano, 2002; Yadav and Rishiwal, 2011) dynamically forms a network without any support of central management. Because of this dynamic nature, routing in MANET is difficult when some QoS requirements need to be satisfied. There are many routing protocols that can fulfill various QoS requirements but none of those protocols supports those requirements altogether. In this paper, an enhanced technique combining Ad-hoc On Demand Distance Vector (AODV) with Ant Colony Optimization (ACO) (Dorigo and Stu1etzle, 2004) mechanism is developed. In proposed technique, before a node sends a data packet to another node, like AODV, it finds out the route to the destination. For finding the route, the sender node broadcasts Route Request packets (named as ReqAnt packets) in the network. This is same as biological ants which first spread out in many directions for searching food. After finding the food source, they deposit pheromone on their way back to the home, so that other ants can get knowledge about the path. Similarly, in the proposed technique the ReqAnt packets are broadcasted in the network. They collect information about the path namely: end to end reliability of the route, congestion along the route, residual energy of the node along the route and length of the route. After receiving the ReqAnt packet, the destination calculates pheromone count of a route considering the path information stored in ReqAnt and sends back the RepAnt (Route Reply packet). The route of highest pheromone count is considered for routing data packets. The ACO based Enhanced AODV outperforms AODV and other ACO routing schemes in various scenarios. Organization of the paper is as follows. Section 2 presents the problem statement of the research work. Section 3 discusses some of the existing schemes. The proposed enhanced ANT AODV scheme is presented in section 4. In section 5 the simulation results of the proposed scheme is compared with AODV, DSR and enhanced ANT DSR. Finally, in section 6 conclusion and future work has been presented.

The thermal stress of the system will be enlarged with the increase of heat flux in the circuit board [1], resulting in a significant increase of the circuit failure rate. However, electronic engineers usually neglect the steady-state temperature variety caused by the placement of components or just simply separate the chips with high power consumption. In addition, we can also use computer simulation software to calculate the temperature, such as Flotherm [2]. However, it’s difficult to deal with the overlapping region when the number of components is significant and the range of optimization are relatively large. In order to overcome these questions, many layout optimization algorithms have been researched which can be divided into optimization algorithm and heuristic algorithm. The optimization algorithms take advantage of operational research knowledge and can get the optimal solution of the layout accurately. However, when the PCB area is large, the time complexity is also high. The heuristic algorithm can discover the sub optimal solution of the feasible solution space by guiding rules under the acceptable cost of the system. In a complex scene, heuristic algorithms are more suitable for engineering applications such as genetic algorithms [3, 4], simulated annealing algorithms [5] and ant colony algorithms [6, 7]. Based on the principle of heuristic optimization, many PCB layout optimization methods have been proposed. Bogula, Chermoshencev and Suzdaltsev [8] is devoted to the development and study of evolutionary algorithms for solving multi-objective problems of high-speed digital electronic PCB design. Ismail and Yusof [9, 10] choose the Weighted-sum approach to consider the temperature and the inter-connection between each component, but perhaps some devices not only have high power consume but also should be put the central position. Alexandridis, Paizis, Chondrodima and Stogiannos [11] developed an approach dealing with the continuous nature placement in PCB thermal design based on the particle swarm optimization. The above algorithms can solve the device layout problem in a certain scene, but does not consider the existence of a priori layout constraints between the devices on the printed circuit board. In this paper, we propose a new partial grid model method and use the ant colony algorithm to optimize the layout of devices. For the actual requirements of the component, we place them in the designated area. Some components have a “package” relationship, which need to be placed in the adjacent area. Other components without specific location restrictions can be placed over a large area. In addition, in order to avoid the local optimal solution, this paper use the ant colony method with classified compensation. Finally, the Flotherm simulation experiments show that the optimized device placement can effectively reduce the maximum temperature and average temperature on the printed circuit board, which proves the validity of our algorithm.

Abstract:

The objective of this paper is to find a reasonable path to recovery of dangerous goods, reduce the possible loss in the process of transportation. The research on the route of dangerous goods recycling exiting usually use the traditional vehicle routing ploblem (Vehicle Routing Problem, VRP) model, this greatly increases the dangerous goods transport vehicles in the storage time, the recovery of risks in the process of cost increases to a certain extent, so use the complete third party logistics outsourcing ( 3PL ) way of handing to deal with dangerous goods transportation problem, then build a new multi-objective optimization model, use the inverse selection operation of ant colony algorithm( ACO-nso ) to solve the model, get the Pareto optimal solution sets. An example is given to demonstrate the effectiveness of the proposed model and algorithm.