Parameter selection and performance comparison of particle swarm optimization in sensor networks localization

Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors\u27 memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm

Small world network based dynamic topology for particle swarm optimization

Abstract: A new particle optimization algorithm with dynamic topology is proposed based on ‘small world’ network. The technique imitates the dissemination of information in a ‘small world network’ by dynamically updating the neighborhood topology of particle swarm optimization. The proposed dynamic neighborhood strategy can effectively coordinate the exploration and exploitation ability of particle swarm optimization. Simulations demonstrated that convergence of the swarms is guaranteed. Experiments demonstrated that the proposed method maintained the population diversity and enhanced the global search ability

Application of Particle Swarm Techniques in Sensor Network Configuration

A decentralized version of particle swarm optimization called the distributed particle swarm optimization (DPSO) approach is formulated and applied to the generation of sensor network configurations or topologies so that the deleterious effects of hidden nodes and asymmetric links on the performance of wireless sensor networks are minimized. Three different topology generation schemes, COMPOW, Cone-Based and the DPSO--based schemes are examined using ns-2. Simulations are executed by varying the node density and traffic rates. Results contrasting heterogeneous vs. homogeneous power reveal that an important metric for a sensor network topology may involve consideration of hidden nodes and asymmetric links, and demonstrate the effect of spatial reuse on the potency of topology generators

Layout Optimization of Braced Frames Using Differential Evolution Algorithm and Dolphin Echolocation Optimization

In this study, topology optimization is applied to concentrically braced frames in order to find economical solutions for conventional structural steel frames. Dierential Evolution Algorithm and Dolphin Echolocation Optimization are applied for structural optimization. Numerical examples are studied and results of comparison with other meta-heuristic algorithms, including Genetic Algorithm, Ant colony optimization, Particle Swarm, and Big Bang-Big Crunch are presented

Comparative Study of Particle Swarm Optimization Algorithms in Solving Size, Topology, and Shape Optimization

This paper focuses on optimizing truss structures while propose best PSO variants. Truss optimization is one way to make the design efficient. There are three types of optimization, size optimization, shape optimization, and topology optimization. By combining size, shape and topology optimization, we can obtain the most efficient structure. Metaheuristics have the ability to solve this problem. Particle swarm optimization (PSO) is metaheuristic algorithm which is frequently used to solve many optimization problems. PSO mimics the behavior of flocking birds looking for food. But PSO has three parameters that can interfere with its performance, so this algorithm is not adaptive to diverse problems. Many PSO variants have been introduced to solve this problem, including linearly decreasing inertia weight particles swarm optimization (LDWPSO) and bare bones particles swarm optimization (BBPSO). The metaheuristic method is used to find the solution, while DSM s used to analyze the structure. A 10-bar truss structure and a 39-bar truss structure are considered as case studies. The result indicates that BBPSO beat other two algorithms in terms of best result, consistency, and convergence behaviour in both cases. LDWPSO took second place for the three categories, leaving PSO as the worst algorithm that tested

Effect of parameter selection on different topological structures for Particle Swarm Optimization algorithm

Abstract Particle Swarm Optimization is an evolutionary optimization algorithm, largely studied during the years: analysis of convergence, determination of the optimal coefficients, hybridization of the original algorithm and also the determination of the best relationship structure between the swarm elements (topology) have been investigated largely. Unfortunately, all these studies have been produced separately, and the same coefficients, derived for the original topology of the algorithm, have been always applied. The intent of this paper is to identify the best set of coefficients for different topological structures. A large suite of objective functions are considered and the best compromise coefficients are identified for each topology. Results are finally compared on the base of a practical ship design application

Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks

Copyright @ Springer-Verlag Berlin Heidelberg 2010.The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council(EPSRC) of UK under Grant EP/E060722/1

Integration of Genetic Algorithm and Cultural Particle Swarm Algorithms for Constrained Optimization of Industrial Organization and Diffusion Efficiency Analysis in Equipment Manufacturing Industry

Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to "premature", but also overcomes the drawback of penalty function method. Firstly, improved topology structure of Particle swarm optimization algorithm. Secondly, using crossover strategy and niche competition mechanism. Verified by the test functions, the proposed algorithm has good performance. Through the analysis of the manufacturing performance based on the algorithm, the paper proposes some optimization strategies such as improving the manufacturing industry market concentration, improving the manufacturing level of industry product differentiation and so on

Particle Swarm Optimization with Quantum Infusion for the Design of Digital Filters

In this paper, particle swarm optimization with quantum infusion (PSO-QI) has been applied for the design of digital filters. In PSO-QI, Global best (gbest) particle (in PSO star topology) obtained from particle swarm optimization is enhanced by doing a tournament with an offspring produced by quantum behaved PSO, and selecting the winner as the new gbest. Filters are designed based on the best approximation to the ideal response by minimizing the maximum ripples in passband and stopband of the filter response. PSO-QI, as is shown in the paper, converges to a better fitness. This new algorithm is implemented in the design of finite impulse response (FIR) and infinite impulse response (IIR) filter

Integration of Genetic Algorithm and Cultural Particle Swarm Algorithms for Constrained Optimization of Industrial Organization and Diffusion Efficiency Analysis in Equipment Manufacturing Industry

Abstract: Aiming at industrial organization multi-objective optimization problem in Equipment Manufacturing Industry, The paper proposes a new type of double layer evolutionary cultural particle swarm optimization algorithm. The algorithm combines the advantages of the particle swarm optimization algorithm and cultural algorithm. It not only revises the problem that the particles are easy to "premature", but also overcomes the drawback of penalty function method. Firstly, improved topology structure of Particle swarm optimization algorithm. Secondly, using crossover strategy and niche competition mechanism. Verified by the test functions, the proposed algorithm has good performance. Through the analysis of the manufacturing performance based on the algorithm, the paper proposes some optimization strategies such as improving the manufacturing industry market concentration, improving the manufacturing level of industry product differentiation and so on