A constraint-handling mechanism for particle swarm optimization

Toscano-Pulido, Gregorio; Coello, Carlos A. Coello

doi:10.1109/cec.2004.1331060

Cited by 121 publications

(75 citation statements)

References 12 publications

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“…The mechanism proposed in paper [26] was adopted to help the considered PSO methods of this paper to solve the functions with constraints. This mechanism is used to select the leaders, and it is based both on the feasible solutions and on the fitness value of a particle.…”

Section: Mathematical Expression Global Minimummentioning

confidence: 99%

“…It can be found that the convergence speed of EQPSO is faster than other considered PSO methods. EQPSO and the other considered PSO methods were also used to optimize the problems with constraints, and three functions with constraints of paper [26] (g07, g09 and g10, which are shown in Table 9) are used as the test functions. …”

Section: Mathematical Expression Global Minimummentioning

confidence: 99%

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An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

2015

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Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the number of iterations is limited. A novel way of computing the local attractor for QPSO is proposed to improve QPSO's performance in global searching, and this novel QPSO is denoted as EQPSO during which we can guarantee the particles are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iteration. We also discuss this way of computing the local attractor in mathematics. The results of test functions are compared between EQPSO and other optimization techniques (including six different PSO and seven different optimization algorithms), and the results found by the EQPSO are better than other considered methods.

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Section: Mathematical Expression Global Minimummentioning

confidence: 99%

Section: Mathematical Expression Global Minimummentioning

confidence: 99%

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

2015

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“…In this paper, objective function is optimized by constrained PSO algorithm [11,12] . Firstly, J particles are initialized in the target search space of N N β β µ µ .…”

Section: The Objective Function Optimizationmentioning

confidence: 99%

“…From the viewpoint of maximum likelihood estimation, the objective function is derived. By constraint particle swarm optimization [11,12] to maximize the objective function, we can get the estimated mixing matrix. The proposed method is not sensitive to the initial value, so the average mean square error of the mixing matrix estimation is smaller than traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Mixing Matrix Estimation in Blind Source Separation Based on Generalized Gaussian Mixture Modal

Chen

Liu

2012

AEF

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Abstract. The accurate estimation of mixing matrix is critical for blind separation, for solving the problems of traditional methods such as bad robustness and low accuracy, a method based on statistical modal is proposed. The generalized Gaussian mixture modal is used to fit the distribution of single-source-points, a new objective function for clustering is obtained from the view of maximum likelihood estimation. Constrained particle swarm optimization is used to optimize the objective function, by which the mixing matrix is estimated. This method is applicable to determined and underdetermined blind source separation. The simulation shows that the proposed method has higher estimation accuracy and is more robust than traditional methods.

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“…This method is problem dependent; however its results are generally superior to those obtained through stationary functions. In Toscano and Coello's PSO algorithm [141], if both particles compared are infeasible, then the particle that has the lowest value in its total violation of constraints wins. One major disadvantage of using penalty functions, in which case all constraints must be combined into a single objective function (this is also called the weighted-sum approach), is that a user must specify a weight coefficient for each constraint.…”

Section: Pso For Constraint Handlingmentioning

confidence: 99%

Swarm Intelligence in Optimization

Blum

Natural Computing Series

335

156

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Summary. Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. In this chapter we focus on two of the most successful examples of optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization. Ant colony optimization was introduced as a technique for combinatorial optimization in the early 1990s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. In addition, particle swarm optimization was introduced for continuous optimization in the mid-1990s, inspired by bird flocking.

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A constraint-handling mechanism for particle swarm optimization

Cited by 121 publications

References 12 publications

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

Mixing Matrix Estimation in Blind Source Separation Based on Generalized Gaussian Mixture Modal

Swarm Intelligence in Optimization

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