Voting-mechanism based ensemble constraint handling technique for real-world single-objective constrained optimization
Xupeng Wen, Guohua Wu, Mingfeng Fan, Rui Wang, Ponnuthurai Nagaratnam Suganthan · 2020
Constraint handling techniques are of great significance in efficiently solving constrained optimization problems. This paper proposes a novel ensemble framework for constraint handling techniques based on voting-mechanism, in which four popular constraint handling techniques are included. Each of the constituent constraint handling techniques votes for the solutions at each generation based on its own rules. Solutions getting more votes are regarded as promising individuals and survive to the next generation. This ensemble framework based on voting-mechanism reflects the collective wisdom in decision-making of human beings. In addition, a differential evolution (DE) variant is designed as the search engine, in which four search strategies are combined to generate new individuals and maintain the balance between diversity and convergence of the population. The proposed algorithm has been tested on 57 real world single-objective constraint optimization problems and 7 problems are selected using variable reduction strategy (VRS). The experiment shows that the proposed algorithm achieves competitive performance, indicating that the voting-mechanism ensemble constraint handling technique combine DE algorithm together can effectively deal with constrained optimization problems.