Abstract
It is generally known that the balance between convergence and diversity is a key issue for solving multi-objective optimization problems. Thus, a chaotic multi-objective particle swarm optimization approach incorporating clone immunity (CICMOPSO) is proposed in this paper. First, points in a non-dominated solution set are mapped to a parallel-cell coordinate system. Then, the status of the particles is evaluated by the Pareto entropy and difference entropy. At the same time, the algorithm parameters are adjusted by feedback information. At the late stage of the algorithm, the local-search ability of the particle swarm still needs to be improved. Logistic mapping and the neighboring immune operator are used to maintain and change the external archive. Experimental test results show that the convergence and diversity of the algorithm are improved.
Highlights
Most problems [1,2,3,4,5,6] in engineering and science are multi-objective optimization problems, and these objectives usually conflict with each other
In order to test the performance of the CICMOPSO algorithm, the ZDT [27] (Table 2) and DTLZ [28]
For ZDT4, CICMOPSO was worse than NICPSO, but for other functions, CICMOPSO had the better performance
Summary
Most problems [1,2,3,4,5,6] in engineering and science are multi-objective optimization problems, and these objectives usually conflict with each other. When solving multi-objective optimization problems using intelligent algorithms, there are three goals that need to be satisfied:. The particle swarm optimization (PSO) algorithm [11,12] is one of the most important and studied paradigms in computational swarm intelligence. PSO algorithms, there are six research directions [13]: (1) aggregating approaches, which combine all the objectives of the problem into a single one. (4) Pareto-based approaches, which use leader selection techniques based on Pareto dominance This method is the mainstream method, such as multi-objective particle swarm (MOPSO) [14].
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