A Hybrid of Random Search and PSO for Solving Constrained Multi-Objective Optimization Problems
نویسنده
چکیده
Suitable solutions for multi-objective optimization problems are investigated as a set of solutions in the literature, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. The proposed algorithm in this paper is a hybrid of the particle swarm optimization algorithm [1] and a random search technique with quadratic approximation formula[2] named Random Search Quadratic approximation Particle Swarm Optimization (RQPSO) algorithm. In this proposed algorithm, a probability having a certain value provided by the user has been fixed. In every iteration, if the uniformly generated random number r(0,1) is less than that value , then the velocity vector is generated by the standard PSO algorithm otherwise it is generated by random search technique with quadratic approximation formula [2]. The proposed algorithm is tested on 13 test problems taken from the literature and are listed in the paper. Results from the proposed algorithm are compared with the known results from the literature and it has been observed that the proposed algorithm improves its performance in number of cases. KeywordsParticle Swarm Optimization(PSO), Multi-objective optimization problems(MOOP) and Random Search Quadratic approximation Particle Swarm Optimization (RQPSO).
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