ABC plus ES: Combining Artificial Bee Colony Algorithm and Evolution Strategies on Engineering Design Problems and Benchmark Functions

被引：5

作者：

Mollinetti, Marco Antonio Florenzano ^{[1
]}

Souza, Daniel Leal ^{[2
]}

Pereira, Rodrigo Lisboa ^{[2
]}

Kudo Yasojima, Edson Koiti ^{[2
]}

Teixeira, Otavio Noura ^{[3
]}

机构：

[1] Univ Tsukuba, Lab Syst Optimizat, Tsukuba, Ibaraki, Japan

[2] Fed Univ Para UFPA, Inst Exact & Nat Sci ICEN, Belem, Para, Brazil

[3] Fed Univ Para, Tucurui Campus, Tucurui, PA, Brazil

来源：

HYBRID INTELLIGENT SYSTEMS, HIS 2015 | 2016年 / 420卷

关键词：

PARTICLE SWARM OPTIMIZATION;

D O I：

10.1007/978-3-319-27221-4_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The following paper introduces a hybrid algorithm that combines Artificial Bee Colony Algorithm (ABC) and a model of Evolution Strategies (ES) found in the Evolutionary Particle Swarm Optimization (EPSO), another hybrid metaheuristic. The goal of this approach is to incorporate the effectiveness and simplicity of the ABC with the thorough local search mechanism of the Evolution Strategies in order to devise an algorithm that is able to achieve better optimality in less time than the original ABC applied to function optimization problems. With the intention of assessing this novel algorithm performance and reliability, several unconstrained benchmark functions as well as four large-scale constrained optimization-engineering problems (WBD, DPV, SRD-11 and MWTCS) act as an evaluation environment. The results obtained by the ABC+ES are compared to original ABC and several other optimization techniques.

引用

页码：53 / 66

页数：14

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