A Large Scale Evolutionary Algorithm Based on Determinantal Point Processes for Large Scale Multi-Objective Optimization Problems

Global optimization challenges are frequent in scientific and engineering areas where loads of evolutionary computation methods i.e., differential evolution (DE) and particle-swarm optimization (PSO) are employed to handle these problems. However, the performance of these algorithms declines due to...

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Published inElectronics (Basel) Vol. 11; no. 20; p. 3317
Main Authors Okoth, Michael, Shang, Ronghua, Jiao, Licheng, Arshad, Jehangir, Rehman, Ateeq, Hamam, Habib
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2022
Subjects
Algorithms
Analysis
Decomposition
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Global optimization
Mathematical optimization
Multiple criteria decision making
Multiple objective analysis
Optimization algorithms
Pareto optimization
Performance evaluation
Population
Sorting algorithms
Variables
China
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ISSN2079-9292
2079-9292
DOI10.3390/electronics11203317

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Summary:Global optimization challenges are frequent in scientific and engineering areas where loads of evolutionary computation methods i.e., differential evolution (DE) and particle-swarm optimization (PSO) are employed to handle these problems. However, the performance of these algorithms declines due to expansion in the problem dimension. The evolutionary algorithms are obstructed to congregate with the Pareto front rapidly while using the large-scale optimization algorithm. This work intends a large-scale multi-objective evolutionary optimization scheme aided by the determinantal point process (LSMOEA-DPPs) to handle this problem. The proposed DPP model introduces a mechanism consisting of a kernel matrix and a probability model to achieve convergence and population variety in high dimensional relationship balance to keep the population diverse. We have also employed elitist non-dominated sorting for environmental selection. Moreover, the projected algorithm also demonstrates and distinguishes four cutting-edge algorithms, each with two and three objectives, respectively, and up to 2500 decision variables. The experimental results show that LSMOEA-DPPs outperform four cutting-edge multi-objective evolutionary algorithms by a large margin.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11203317