TY - GEN
T1 - Fireworks Algorithm for Multimodal Optimization Using a Distance-based Exclusive Strategy
AU - Yu, Jun
AU - Takagi, Hideyuki
AU - Tan, Ying
PY - 2019/6
Y1 - 2019/6
N2 - We propose a distance-based exclusive strategy to extend fireworks algorithm as a niche method to find out multiple global/local optima. This strategy forms sub-groups consisting of a firework individual and its generated spark individuals, each sub-group is guaranteed not to search overlapped areas each other. Finally, firework individuals are expected to find different global/local optima. The proposed strategy checks the distances between a firework and other fireworks which fitness is better than that of the firework. If the distance between two firework individuals is shorter than the sum of their searching radius, i.e. amplitude of firework explosions, these two firework individuals are considered to search overlapped area. Thus, the poor firework is removed and replaced by its opposite point to track multiple optima. To evaluate the performance of our proposed strategy, enhanced fireworks algorithm (EFWA) is used as a baseline algorithm and combined with our proposal. We design a controlled experiment, and run EFWA and (EFWA + our proposal) on 8 benchmark functions from CEC 2015 test suite, that is dedicated to single objective multi-niche optimization. The experimental results confirmed that the proposed strategy can find multiple different optima in one trial run.
AB - We propose a distance-based exclusive strategy to extend fireworks algorithm as a niche method to find out multiple global/local optima. This strategy forms sub-groups consisting of a firework individual and its generated spark individuals, each sub-group is guaranteed not to search overlapped areas each other. Finally, firework individuals are expected to find different global/local optima. The proposed strategy checks the distances between a firework and other fireworks which fitness is better than that of the firework. If the distance between two firework individuals is shorter than the sum of their searching radius, i.e. amplitude of firework explosions, these two firework individuals are considered to search overlapped area. Thus, the poor firework is removed and replaced by its opposite point to track multiple optima. To evaluate the performance of our proposed strategy, enhanced fireworks algorithm (EFWA) is used as a baseline algorithm and combined with our proposal. We design a controlled experiment, and run EFWA and (EFWA + our proposal) on 8 benchmark functions from CEC 2015 test suite, that is dedicated to single objective multi-niche optimization. The experimental results confirmed that the proposed strategy can find multiple different optima in one trial run.
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U2 - 10.1109/CEC.2019.8790312
DO - 10.1109/CEC.2019.8790312
M3 - Conference contribution
T3 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
SP - 2215
EP - 2220
BT - 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Congress on Evolutionary Computation, CEC 2019
Y2 - 10 June 2019 through 13 June 2019
ER -