A novel x-shaped binary particle swarm optimization

Information Sciences, 2020

Binary Particle swarm optimization (BPSO) is one of the most popular swarm intelligence algorithms to solve binary optimization problems. It has a few parameters, simple structure, and high execution speed. A transfer function is applied in BPSO to convert the continuous search space to the binary one. This algorithm and its variants can sometimes find local optima or exhibit slow convergence speed. Thus, many researchers have improved the structure of BPSO and its transfer function to overcome these shortcomings. In this study, a new time-varying mirrored S-shaped transfer function for BPSO (TVMS-BPSO) is introduced to enhance global exploration and local exploitation in the algorithm. The performance of the proposed transfer function has been compared with some well-known BPSO algorithms and binary meta-heuristic algorithms. These algorithms have been evaluated by CEC 2005 benchmark functions and set of 0–1 multidimensional knapsack problem (MKP) benchmark instances. The experimental results showed that the new transfer function significantly enhances the efficiency of BPSO for both local and global topologies in terms of solution accuracy and convergence speed.

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Particle Swarm Optimization (PSO) algorithm, originated as a simulation of a simplified social system, is an evolutionary computation technique developed successfully in recent years and have been applied to many optimization problems. PSO can be applied to continuous and discrete optimization problems through local and global models. In this paper, PSO is addressed in details. There are some difficulties with the standard PSO where causing slow convergence rate on some optimization problems. These difficulties are transferred to the origin binary PSO (BPSO) that makes the algorithm not to converge well. Due to these difficulties with the BPSO, in this paper a new BPSO (NBPSO) is introduced. Several benchmark problems including unimodal and multimodal functions are considered for testing the robustness and effectiveness of the proposed method over the original BPSO. The results show that NBPSO performs much better than BPSO. Since the obtained results show that NBPSO may trap in the local optima, further modification is carried out. Two different methods are suggested to improve NBPSO which are denoted as Guaranteed Convergence BPSO (GCBPSO) and Improved NBPSO (INBPSO). The results show the superiority of the INBPSO for solving optimization problems.

Information Sciences, 2015

In recent decades, many researchers have been interested in algorithms inspired by the observation of natural phenomena to solve optimization problems. Among them, meta-heuristic algorithms have been extensively applied in continuous (real) and discrete (binary) search spaces. Such algorithms are appropriate for global searches because of their global exploration and local exploitation abilities. In this study, a memetic binary particle swarm optimization (BPSO) scheme is introduced based on hybrid local and global searches in BPSO. The algorithm, binary hybrid topology particle swarm optimization (BHTPSO), is used to solve the optimization problems in the binary search spaces. In addition, a variant of the proposed algorithm, binary hybrid topology particle swarm optimization quadratic interpolation (BHTPSO-QI), is proposed to enhance the global searching capability. These algorithms are tested on two set of problems in the binary search space. Several nonlinear high-dimension functions and benchmarks for the 0-1 multidimensional knapsack problem (MKP) are employed to evaluate their performances. Their results are compared with some well-known modified binary PSO and binary gravitational search algorithm (BGSA). The experimental results showed that the proposed methods improve the performance of BPSO in terms of convergence speed and solution accuracy.

Control & Automation, …, 2007

Particle Swarm Optimization, 2009

2007

A novel competitive approach to particle swarm optimization (PSO) algorithms is proposed in this paper. The proposed method uses extrapolation technique with PSO (ePSO) for solving optimization problems. By considering the basics of the PSO algorithm, the current particle position is updated by extrapolating the global best particle position and the current particle positions in the search space. The position of the particles in each iteration is updated directly without using the velocity equation. The position equation is formulated with the global best (gbest) position, personal or local best position (pbest) and the current position of the particle. The proposed method is tested with a set of five standard optimization bench mark problems and the results are compared with those obtained through three PSO algorithms, the canonical PSO (cPSO), the global-local best PSO (GLBest-PSO) and the proposed ePSO method. The cPSO includes a time varying inertia weight (TVIW) and time varying acceleration coefficients (TVAC) while the GLBest PSO consists of global-local best inertia weight (GLBest 1W) with global-local best acceleration coefficient (GLBestAC). The simulation results clearly elucidate that the proposed method produces the near global optimal solution. It is also observed from the comparison of the proposed method with cPSO and GLBest-PSO, the ePSO is capable of producing a quality of optimal solution with faster convergence rate. To strengthen the comparison and prove the efficacy of the proposed method, analysis of variance and hypothesis t-test are also carried out. All the results indicate that the proposed ePSO method is competitive to the existing PSO algorithms.

Binary particle swarm optimization Transfer function The 0–1 knapsack problem The truss optimization problem a b s t r a c t Many real-world problems belong to the family of discrete optimization problems. Most of these problems are NP-hard and difficult to solve efficiently using classical linear and convex optimization methods. In addition, the computational difficulties of these optimization tasks increase rapidly with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsi-cally multimodal and non-convex. In such a case, it is more desirable to have an effective optimization method that can cope better with these problem characteristics. Binary particle swarm optimization (BPSO) is a simple and effective discrete optimization method. The original BPSO and its variants have been used to solve a number of classic discrete optimization problems. However, it is reported that the original BPSO and its variants are unable to provide satisfactory results due to the use of inappropriate transfer functions. More specifically, these transfer functions are unable to provide BPSO a good balance between exploration and exploitation in the search space, limiting their performances. To overcome this problem, this paper proposes to employ a time-varying transfer function in the BPSO, namely TV T-BPSO. To understand the search behaviour of the TV T-BPSO, we provide a systematic analysis of its exploration and exploitation capability. Our experimental results demonstrate that TV T-BPSO outperforms existing BPSO variants on both low-dimensional and high-dimensional classical 0–1 knapsack problems, as well as a 200-member truss problem, suggesting that TV T-BPSO is able to better scale to high dimensional combinatorial problems than the existing BPSO variants and other metaheuristic algorithms.

Journal of Global Optimization, 2013

Your article is protected by copyright and all rights are held exclusively by Springer Science +Business Media New York. This e-offprint is for personal use only and shall not be selfarchived in electronic repositories. If you wish to self-archive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com".

Journal of Computer Science, 2013

Particle Swarm Optimization used to solve a continuous problem and has been shown to perform well however, binary version still has some problems. In order to solve these problems a new technique called New Binary Particle Swarm Optimization using Immunity-Clonal Algorithm (NPSOCLA) is proposed This Algorithm proposes a new updating strategy to update the position vector in Binary Particle Swarm Optimization (BPSO), which further combined with Immunity-Clonal Algorithm to improve the optimization ability. To investigate the performance of the new algorithm, the multidimensional 0/1 knapsack problems are used as a test benchmarks. The experiment results demonstrate that the New Binary Particle Swarm Optimization with Immunity Clonal Algorithm, found the optimum solution for 53 of the 58 multidimensional 0/1knapsack problems.