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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenting Mo Sheng-Uei Guan |
| Copyright Year | 2006 |
| Description | Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore (Wenting Mo; Sheng-Uei Guan) |
| Abstract | This paper presents a new evolutionary approach for function optimization problems particle swarm assisted incremental evolution strategy (PIES). Two strategies are proposed. One is incremental optimization that the whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the number of variables in maximum. Each phase is composed of two stages: in the single-variable evolution (SVE) stage, a population is evolved with respect to one independent variable in a series of cutting planes; in the multi-variable evolving (MVE) stage, the initial population is formed by integrating the population obtained by the SVE in current phase and by the MVE in the last phase. And then the MVE is taken on the incremented variable set. The second strategy is a hybrid of particle swarm optimization (PSO) and the evolution strategy (ES). PSO is applied to adjust the cutting planes (in SVEs) or hyper-planes (in MVEs) while ES is applied to searching optima in the cutting planes/hyper-planes. The results of experiments show that PIES generally outperforms three other evolutionary algorithms, improved normal GA, PSO and SADEXERAF, in the sense that PIES finds solutions with more optimal objective values and closer to the true optima |
| Sponsorship | IEEE Singapore Sect. IEEE Singapore SMC Chap. IEEE Singapore RA Chap |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 303148 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424400236 |
| DOI | 10.1109/ICCIS.2006.252276 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-06-07 |
| Publisher Place | Thailand |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle swarm optimization Evolution (biology) Evolutionary computation Electronic switching systems Robustness Neural networks Biological cells Convergence Multi-variable evolution (MVE Evolution strategy Particle swarm optimization Incremental optimization Single-variable evolution (SVE) |
| Content Type | Text |
| Resource Type | Article |
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