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Optimization in Dynamic Environments Utilizing a Novel Method Based on Particle Swarm Optimization

Danial Yazdani, Babak Nasiri, Reza Azizi, Alireza Sepas-Moghaddam, Mohammad Reza Meybodi


In numerous real world optimization problems, objective function or constraints of the problem can be changed during time. If these undefined situations are occurred in optimization process, this problem is called dynamic. There are several challenges in dynamic environments optimization, so that algorithms designed for optimization in these environments would utilize several mechanisms in order to conquer the challenges. In this paper, a novel algorithm for optimization in dynamic environments is proposed based on particle swarm optimization in which a novel mechanism have been used for improving the performance. In this mechanism, it is tried to increase the ability of local search around optimum with focusing on best found peak in each environments. The results of the proposed approach are evaluated on moving peak benchmarks and are compared with results of several state of the art algorithms. Experimental results show the superiority of the proposed method.


Dynamic optimization problems, particle swarm optimization, moving peaks benchmark, dynamic environments, swarm intelligence, Local Search.

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