Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

Wei Li,Soroosh Mahmoodi,Ying Huang,Xiang Meng

doi:10.1007/s40747-020-00263-z

Abstract

Multiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

Highlights

Multiobjective optimization problems (MOPs) are complex optimization problems that are widely used but difficult to solve in the real world
The results demonstrate that the KGMOPSO algorithm possesses a strong competitive advantage in solving DTLZ test problems with three optimization objectives
The KGMOPSO algorithm only obtains the best inverted generational distance (IGD) values on DTLZ6 and DTLZ7 functions, for HV, which is shown in Table 7, compared with other multiobjective algorithms, the proposed KGMOPSO algorithm obtains the best results on DTLZ2, DTLZ4 and DTLZ7

Summary

Introduction

Multiobjective optimization problems (MOPs) are complex optimization problems that are widely used but difficult to solve in the real world. They have gradually garnered the attention of researchers due to their challenging nature in that they require the efficient and effective optimization of multiple conflicting objectives simultaneously. For single-objective optimization problems (SOPs), the goal is to find a global optimal solution. There is no absolute optimal solution for MOPs because the optimization of one objective may deteriorate the performance of other objectives. The goal of solving MOPs is to obtain a set of equivalent tradeoff solutions, which are called Pareto optimal solutions.

Results

Discussion

Conclusion