Differential Evolution Algorithm for Multiple Inter-dependent Components Traveling Thief Problem
Ismail Mohamed Ali, Daryl Essam, Kathryn Kasmarik · 2020
Differential evolution was mainly proposed for solving optimization problems with continuous decision variables because of its Euclidean distance-based learning concept. This made it unsuitable for many binary and discrete problems. However, several studies approved the applicability of differential evolution algorithm for effectively solving such problems. In this paper, a new design of differential evolution, which incorporates mapping and repairing methods, modified mutation operator and local searches, is proposed to solve the complex multicomponents traveling thief problems that are characterized by both binary and discrete parameters. Also, a novel initialization and repairing method, which enables differential evolution's operators to only evolve solutions of one component and optimally distribute/update the solutions of the other one with considering the inter-dependency between both components, is introduced. To judge the performance of the proposed algorithm, 13 strongly correlated instances of traveling thief problems have been solved and the results have been compared with those from 24 selfdesigned and state-of-the-art algorithms. Results demonstrated the competitive performance of the proposed algorithm in terms of the quality of obtained solutions and computational time.