Abstract
Multi-Objective Particle Swarm Optimizers (MOPSOs) are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ε-dominance based MOPSO (CEPSO) is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs) are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ε-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.
Highlights
Multi-objective Optimization Problems (MOPs) are a class of problems frequently encountered in various fields of science and technologies
Researches on MultiObjective Particle Swarm Optimizers (MOPSOs) is grounded on the successful application of Particle Swarm Optimizer (PSO)[4], a population based stochastic optimization technique developed by Kennedy and Eberhart in 19955, and the fact that several improved PSOs are proven to produce very good results at a very low computational cost
coevolution and -dominance based MOPSO (CEPSO) is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems while the number of function evaluations just increases linearly with respect to the number of decision variables
Summary
Multi-objective Optimization Problems (MOPs) are a class of problems frequently encountered in various fields of science and technologies Such problems can be very complex when certain pragmatic functions and specific model constraints come into place. Traditional methods, such as mathematical programming, are robust and have been proved their effectiveness in handling a variety of common MOPs. Traditional methods, such as mathematical programming, are robust and have been proved their effectiveness in handling a variety of common MOPs Such techniques have been found to encounter difficulties such as getting trapped in local optima, intolerable computational complexity, and inapplicable to certain kinds of objective functions.[1] To overcome these shortcomings, heuristic optimization techniques have been developed, among which MultiObjective Particle Swarm Optimizers (MOPSOs) are especially promising.[2,3]. All these MOPSOs are more or less suffered from severe drawbacks including being trapped in local optima, unbearable number of function evaluations and the curse of dimensionality
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