Dynamic Particle Swarm Optimization for Multimodal Function

Omranpour, Hesam; Ebadzadeh, Mohammad Mehdi; Shiry, Saeed; Barzegar, S.

doi:10.11591/ij-ai.v1i1.367

Cited by 15 publications

(12 citation statements)

References 10 publications

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“…We first implement a standard PSO algorithm due to its simplicity and effectiveness in dealing with multi-modal optimization problems. PSO algorithms were originally developed in [ 38 ] inspired by the observation of bird flocking and fish schooling, which are also related to genetic algorithms, and they are widely used in both scientific research [ 66 , 67 ] and engineering applications [ 68 , 69 ]. PSO optimizes a problem by moving solution hypotheses around in the search-space according to the current hypothesis and velocity computed to the present local and global optima.…”

Section: Proposed Methodsmentioning

confidence: 99%

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Fan

Gong

et al. 2015

Sensors

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We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).

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Section: Proposed Methodsmentioning

confidence: 99%

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Fan

Gong

et al. 2015

Sensors

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“…Here, M is the population size and P i is the pbest position of particle i. Then the values of L i,j (t) can be got by (9) and the position of particle can be evaluated by (10) where parameter β is called contraction-expansion coefficient, which can be tuned to control the convergence speed of the algorithm according to different problems. The variant of PSO with equation 10is known as Quantumbehaved Particle Swarm Optimization (QPSO).…”

Section: Quantum-behaved Particle Swarm Optimizationmentioning

confidence: 99%

“…has been a fount of inspiration to other researchers in finding other topologies to improve the performance of PSO [5][6][7][8][9]. Bratton and Kennedy defined a standard PSO, which is an extension of the original PSO algorithm while taking into account the previous developments that can improve the performance of the algorithm [10].…”

Section: Introductionmentioning

confidence: 99%

Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies

Sheng

Sun

et al. 2015

Journal of Algorithms & Computational Technology

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Quantum-behaved particle swarm optimization (QPSO), motivated by analysis from particle swarm optimization (PSO) and quantum mechanics, has shown excellent performance in finding the optimal solutions for many optimization problems. In QPSO, the mean best position, defined as the average of the personal best positions of all the particles in a swarm, is employed as a global attractor to attract the particles to search solutions globally. This paper presents a comprehensive analysis of the mean best position and proposes several novel adaptive strategies to determine the position. In particular, four variants of mean best position are proposed to serve as global attractors and the corresponding parameter selection methods are also provided. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other QPSO and PSO variants. The simulation results show that the proposed QPSO algorithm have some advantages over the original QPSO and other PSO algorithms.

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“…A huge number of variants are proposed and applied in different fields. [2][3][4][5][6][7][8][9] An efficient optimization algorithm should have excellent ability to balance local and global search. For PSO, local search means that the particle is capable to exploit the neighborhood of the present solution for other promising candidates while global search implies some solutions in other part of the search space being able to be found.…”

Section: Introductionmentioning

confidence: 99%

Improved quantum-behaved particle swarm optimization with local search strategy

Sheng

et al. 2016

Journal of Algorithms & Computational Technology

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Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

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Dynamic Particle Swarm Optimization for Multimodal Function

Cited by 15 publications

References 10 publications

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Quantum-Behaved Particle Swarm Optimization with Novel Adaptive Strategies

Improved quantum-behaved particle swarm optimization with local search strategy

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