TY - GEN
T1 - Improved efficiency of mopso with adaptive inertia weight and dynamic search space
AU - Pang, Lee Ping
AU - Ng, Sin Chun
N1 - Publisher Copyright:
© 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2018/7/6
Y1 - 2018/7/6
N2 - In this paper, a new Multi-Objective Particle Swarm optimization algorithm (MOPSO) with adaptive inertia weight and dynamic search space is introduced for multi-objective optimization. The objective of the study is to investigate an efficient MOPSO to deal with large-scale optimization and multi-modal problems. The new adaptive inertia weight strategy allows the inertia weight to keep varying throughout the algorithm process, which helps the algorithm to escape from local optima. The dynamic search space design can avoid decision variables from continuously taking their extreme values, and therefore enhances the searching efficiency. The performance of the proposed algorithm was compared with three popular multi-objective algorithms in solving seven benchmark test functions. Results show that the new algorithm can produce reasonably good approximations of the Pareto front, while performing with a budget of 10,000 fitness function evaluations.
AB - In this paper, a new Multi-Objective Particle Swarm optimization algorithm (MOPSO) with adaptive inertia weight and dynamic search space is introduced for multi-objective optimization. The objective of the study is to investigate an efficient MOPSO to deal with large-scale optimization and multi-modal problems. The new adaptive inertia weight strategy allows the inertia weight to keep varying throughout the algorithm process, which helps the algorithm to escape from local optima. The dynamic search space design can avoid decision variables from continuously taking their extreme values, and therefore enhances the searching efficiency. The performance of the proposed algorithm was compared with three popular multi-objective algorithms in solving seven benchmark test functions. Results show that the new algorithm can produce reasonably good approximations of the Pareto front, while performing with a budget of 10,000 fitness function evaluations.
KW - Bound handing
KW - Exploitation
KW - Exploration
KW - Inertia weight
KW - Multi-objective particle swarm optimization
KW - Searching efficiency
UR - https://www.scopus.com/pages/publications/85051485859
U2 - 10.1145/3205651.3208229
DO - 10.1145/3205651.3208229
M3 - Conference contribution
AN - SCOPUS:85051485859
T3 - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
SP - 1910
EP - 1913
BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -