Abstract

Artificial bee colony (ABC) algorithm is a nature-inspired algorithm that mimics the intelligent foraging behavior of honey bees and it is steadily gaining popularity. It is observed that convergence of ABC algorithm in local minimum is slow. This paper presents an effort to improve the convergence rate of ABC algorithm by integrating differential evolution (DE) operators into it. The proposed ABC-DE algorithm is first tested on three product design optimization problems and the results are compared with co-evolutionary differential evolution (CDE), hybrid particle swarm optimization-differential evolution (PSO-DE) and ABC algorithms. Further, the algorithm is applied on three manufacturing optimization problems, and the results are compared with genetic algorithm (GA), real coded genetic algorithm (RCGA), and RCGA with Laplace Crossover and Power Mutation (LXPM) algorithm and ABC algorithm. Results indicate that ABC-DE algorithm is better than the state of the art algorithms for the aforesaid problems on selected performance metrics.KeywordsArtificial bee colonyDifferential evolutionDesign optimizationManufacturing

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