Adaptive Multi-objective Particle Swarm Optimization Based on Similarity
In the design of multi-objective particle swarm optimization,the correct management of convergence and diversity is the key to obtain a well-distributed approximate solution close to the real Pareto Front.In order to better balance convergence and diversity and prevent premature convergence of algorithms,an adaptive multi-objective particle swarm optimization based on similarity(SAMOPSO)is proposed.Firstly,the algorithm uses the similarity and fitness values between each solution and other solutions in the archive to main-tain the archive and obtain high-quality candidate solutions.Secondly,an adaptive flight parameter adjustment mechanism based on par-ticle information is introduced to further improve the evolution performance of SAMOPSO algorithm.Finally,the SAMOPSO algorithm is compared with six classical multi-objective optimization algorithms on 15 benchmark functions,and the results confirm the effecti-veness of the algorithm.
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