Local Search Strategy Embedded ABC and Its Application in Cost Optimization Model of Project Time Schedule

Tarun K. Sharma and Jitendra Rajpurohit
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Tarun K. Sharma: Amity University Rajasthan, Jaipur, India
Jitendra Rajpurohit: Symbiosis International University, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2019, vol. 10, issue 1, 92-106

Abstract: This article describes how artificial bee colony (ABC) is a promising metaheuristic algorithm, modeled on the intelligent forging behavior of honey bees. ABC takes its inspiration from natural honey bees. In ABC the colony of bees is generally alienated into three groups namely scout, employed and onlooker bees that participates in getting optimal food sources (solutions). With an edge over similar metaheuristic algorithms in solving optimization problems ABC suffers with bad exploitation (local search) capability, however excels in exploration (global search) capability. In order to balance both the aforesaid capabilities, this article embeds the local search strategy in the basic structure of ABC. The proposed scheme is named as LS-ABC. The efficiency of the proposed scheme has been tested and simulated results are compared with state-of-art algorithms over 12 benchmark functions. Also, LS-ABC has been validated to solve cost optimization model of project time schedule. The simulated results are compared with state-of-art algorithms.

Date: 2019
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