TY  - JOUR
ID  - scholars17405
SN  - 18761100
TI  - A Meta Model Based Particle Swarm Optimization for Enhanced Global Search
Y1  - 2022///
A1  - Ahmed, R.
A1  - Mahadzir, S.
A1  - Mohammad Rozali, N.E.
N1  - cited By 1; Conference of 1st International Conference on Artificial Intelligence for Smart Community, AISC 2020 ; Conference Date: 17 December 2020 Through 18 December 2020; Conference Code:286319
SP  - 935
PB  - Springer Science and Business Media Deutschland GmbH
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142698166&doi=10.1007%2f978-981-16-2183-3_88&partnerID=40&md5=0cb4c0a35851b32a8448b0eae4a18ed3
EP  - 944
KW  - Benchmarking; Parameter estimation; Particle swarm optimization (PSO); Stochastic models; Swarm intelligence
KW  -  Benchmark functions; Meta model; Meta-heuristics algorithms; Meta-optimization; Metamodeling; Particle swarm; Particle swarm optimization; Performance; Stochastic optimizations; Swarm optimization
KW  -  Genetic algorithms
N2  - The performance of a metaheuristic algorithm depends on the appropriate selection of its behavioral parameters. A good selection of parameters increases the search ability of an algorithm and avoids premature convergence. Particle swarm optimization (PSO) is swarm-based metaheuristic algorithm, which needs few parameter adjustments and less computational time. Meta-optimization has been used to tune the parameters and to get better results. Previously, authors applied meta optimization techniques to specific problems to tune the parameters and to get better results for specific case studies in different fields, but the application of meta optimization in benchmark functions are limited. The present study proposes meta optimization-based PSO to minimize the computational effort required for manual trial and error-based parameter selection. The proposed algorithm is tested for 14 benchmark functions (8 unimodal and 6 multimodal), and its efficiency and robustness are assessed via statistical analysis. The algorithm outperforms other renowned established algorithms (GA, PSO), and its performance remains consistent with increasing modality and dimensionality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
JF  - Lecture Notes in Electrical Engineering
VL  - 758
AV  - none
ER  -