AUTHORS: Noorazliza Sulaiman, Junita Mohamad-Saleh, Abdul Ghani Abro

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ABSTRACT: Artificial bee colony (ABC) optimization algorithm has been successfully applied to solve various optimization problems. ABC is a kind of bio-inspired algorithm (BIAs) that has attracted the attention of optimization researchers. ABC imitates the foraging behaviour of honeybees, thus it can be classified into swarm-intelligence-based (SI) algorithm; one of the prominent classes in BIAs. ABC has shown tremendous results in comparison with other optimization algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm. Moreover, the advantages of ABC include it is simple and flexible. Despite of these advantages, ABC has been found to be trapped in local optima on multimodal functions and exhibited slow convergence speed on unimodal functions. Motivated by these, researchers have proposed various ABC variants but none of them are able to solve both problems simultaneously. Thus, this paper proposes a new modified ABC algorithm referred to as JA-ABC4 with the objectives to robustly find global optimum and enhance convergence speed. The proposed algorithm has been compared with the standard ABC and other existing ABC variants on twenty-four commonly used benchmarks functions. The performance results have shown that the proposed algorithm has yielded the best performance compared to the standard ABC and two other good ABC variants (BABC1 and IABC) in terms of convergence speed and global minimum achievement. For further justification, JA-ABC4 has been tested to optimize economic environmental dispatch (EED) on 10-unit generator system. The results obtained have also illustrated the robust performance of JA-ABC4 in solving complex real-world problems.

KEYWORDS: Artificial bee colony, swarm-intelligence-based algorithm, convergence speed, economic environmental dispatch

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