Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 26, Issue 1, Pages 43-57Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2021.3087802
Keywords
Encoding; Optimization; Search problems; Statistics; Sociology; Decoding; Traveling salesman problems; Differential evolution (DE); linear ordering problem (LOP); random key (RK) encoding; search space analysis; traveling salesman problem (TSP)
Funding
- Ministry of Education, Youth and Sports of the Czech Republic [LTAIN19176]
- VSB-TU Ostrava [SP2021/24, SP2021/94]
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Differential evolution is a powerful optimization algorithm that has been successfully applied to solve various optimization problems. This study focuses on the application of random key encoding in the differential evolution algorithm to improve its performance in permutation problems.
Differential evolution is a powerful nature-inspired real-parameter optimization algorithm that has been successfully used to solve a number of hard optimization problems. It has been used to tackle both continuous and discrete optimization problems. The application of a continuous method to discrete problems involves several challenges, including solution representation and search space-solution space mapping. In this work, we study random key encoding, a popular encoding scheme that is used to represent permutations in high-dimensional continuous spaces. We analyze the search space it constitutes, study its structure and properties, and introduce two novel modifications of the encoding. We investigate the proposed encoding strategies in the context of four variants of the differential evolution algorithm and demonstrate their usefulness for two widespread permutation problems: 1) the linear ordering problem and 2) the traveling salesman problem.
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