TY - GEN
T1 - Evolutionary algorithm with parallel evaluation strategy of feasible and infeasible solutions considering total constraint violation
AU - Kato, Taiga
AU - Shimoyama, Koji
AU - Obayashi, Shigeru
PY - 2015/9/10
Y1 - 2015/9/10
N2 - A new genetic algorithm to search for Pareto-optimal solutions in multi-objective problems with constraints is proposed. This algorithm employs the parallel evaluation strategy in which feasible and infeasible solutions are preserved in separate populations. Feasible solutions are ranked in accordance with the ordinary non-dominated ranking method. On the other hands, infeasible solutions are ranked based on their objective functions and total constraint violation. The total constraint violation is treated as the (M+1)-th evaluation function in addition to M original objective functions used for ranking infeasible solutions. This non-dominated ranking considering both objective functions and total constraint violation is expected to remove infeasible solutions with large constraint violations and preserve useful solutions. Through the present numerical tests, the proposed algorithm without tunable parameters outperforms the existing genetic algorithms considering either objective functions or constraint violations in multi-objective problems with active constraints. Additionally, the proposed algorithm shows better performance than the genetic algorithm using the penalty approach considering the sum of objective functions and total constraint violation. The improvement of Pareto-optimal solution search capability is accomplished by preserving infeasible solutions near the true Pareto-optimal front restricted by active constraints.
AB - A new genetic algorithm to search for Pareto-optimal solutions in multi-objective problems with constraints is proposed. This algorithm employs the parallel evaluation strategy in which feasible and infeasible solutions are preserved in separate populations. Feasible solutions are ranked in accordance with the ordinary non-dominated ranking method. On the other hands, infeasible solutions are ranked based on their objective functions and total constraint violation. The total constraint violation is treated as the (M+1)-th evaluation function in addition to M original objective functions used for ranking infeasible solutions. This non-dominated ranking considering both objective functions and total constraint violation is expected to remove infeasible solutions with large constraint violations and preserve useful solutions. Through the present numerical tests, the proposed algorithm without tunable parameters outperforms the existing genetic algorithms considering either objective functions or constraint violations in multi-objective problems with active constraints. Additionally, the proposed algorithm shows better performance than the genetic algorithm using the penalty approach considering the sum of objective functions and total constraint violation. The improvement of Pareto-optimal solution search capability is accomplished by preserving infeasible solutions near the true Pareto-optimal front restricted by active constraints.
KW - constraint handling
KW - genetic algorithm
KW - multi-objective optimization
KW - parallel evaluation
KW - total constraint violation
UR - http://www.scopus.com/inward/record.url?scp=84958569095&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958569095&partnerID=8YFLogxK
U2 - 10.1109/CEC.2015.7256997
DO - 10.1109/CEC.2015.7256997
M3 - Conference contribution
AN - SCOPUS:84958569095
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 986
EP - 993
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -