Enhanced Performance of Particle Swarm Optimization with Generalized Generation Gap Model with Parent-Centric Recombination Operator

บทความในวารสาร

ผู้เขียน/บรรณาธิการ

กลุ่มสาขาการวิจัยเชิงกลยุทธ์

ไม่พบข้อมูลที่เกี่ยวข้อง

รายละเอียดสำหรับงานพิมพ์

รายชื่อผู้แต่ง: Phanmak, W.;Worasucheep, C.;Pipopwatthana, C.;Srimontha, S.

ปีที่เผยแพร่ (ค.ศ.): 2012

วารสาร: ECTI Transactions on Computer and Information Technology (2286-9131)

Volume number: 6

Issue number: 2

หน้าแรก: 167

หน้าสุดท้าย: 176

นอก: 2286-9131

บทคัดย่อ

Particle Swarm Optimization (PSO) algorithm has recently gained more attention in the global opti- mization research due to its simplicity and global search ability. This paper proposes a hybrid of PSO and Generalized Generation Gap model with Parent- Centric Recombination operator (G3PCX) [25], a well-known real-coded genetic algorithm. The pro- posed hybrid algorithm, namely PSPG, combines fast convergence and rotational invariance of G3PCX as well as global search ability of PSO. The performance of PSPG algorithm is evaluated using 8 widely-used nonlinear benchmark functions of 30 and 200 deci- sion variables having different properties. The experi- ments study the effects of its new probability parame- ter P x and swarm size for optimizing those functions. The results are analyzed and compared with those from the Standard PSO [14] and G3PCX algorithms. The proposed PSPG with Px = 0.10 and 0.15 can outperform both algorithms with a statistical signif- icance for most functions. In addition, the PSPG is not much sensitive to its swarm size as most PSO al- gorithms are. The best swarm sizes are 40 and 50 for unimodal and multimodal functions, respectively, of 30 decision variables.

คำสำคัญ

Particle Swarm Optimization, Real- Coded Genetic Algorithm, Parent-Centric Recombi- nation, Hybrid algorithm, Optimization.