Robust Modified ABC Variant (JA-ABC5b) for Solving Economic Environmental Dispatch (EED)
Artificial bee colony (ABC) algorithm has been widely used to solve various optimization problems due to its simplicity and flexibility besides showing outrageous results in comparison to other optimization algorithms. Nevertheless, ABC has been found to
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School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia [email protected], [email protected] 2 College of Applied Engineering, King Saud University, Al Muzahimiyah Campus, Riyadh, Kingdom of Saudi Arabia [email protected]
Abstract. Artificial bee colony (ABC) algorithm has been widely used to solve various optimization problems due to its simplicity and flexibility besides showing outrageous results in comparison to other optimization algorithms. Nevertheless, ABC has been found to suffer from few limitations such as slow convergence rates and premature convergence tendency. With the motivation to overcome the problem, this work proposes a modified ABC variant referred to as JA-ABC5b with the aim of robust and faster convergence. The proposed ABC variant has been compared with the standard ABC and other existing ABC variants on 27 benchmarks functions and to solve economic environmental dispatch (EED) problem. The results have shown that JA-ABC5b has the best performance in comparison to the standard ABC and selected existing ABC variants in terms of convergence speed as well as the global optimum achievement besides exhibiting robust performance in solving complex real-world optimization problem. Keywords: Artificial bee colony ABC variants Swarm-intelligence-based algorithm Convergence speed Benchmark functions Economic environmental dispatch
1 Introduction Bio-inspired algorithms (BIAs), inspired by the behaviors of nature have been applied to solve various complex optimization problems [1] as shown by the works of [2–5]. They have been implemented to solve the concern of various problems i.e. the problems of high computational cost and premature convergence tendency faced by numerical methods [4]. The outcomes are the promising results of BIAs at solving those problems [6]. BIAs are metaheuristic method consisting of several classes and one of the most prominent classes is swarm-intelligence-based (SI) algorithms. SI algorithms have been found to show tremendous performance in solving various problems such as the © Springer International Publishing Switzerland 2016 H. Ohwada and K. Yoshida (Eds.): PKAW 2016, LNAI 9806, pp. 55–67, 2016. DOI: 10.1007/978-3-319-42706-5_5
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travelling salesman problem [7], power loss minimization [8], voltage profile improvement [9], economic environmental dispatch [10, 11], stability enhancement for multi-machine power system [12] and many more. A few examples of SI algorithms are ant colony optimization (ACO) [13], particle swarm optimization (PSO) [14] and artificial bee colony (ABC) algorithms [15]. Artificial bee colony (ABC) algorithm, one of the SI methods has recently attracted the attention of optimization researchers due to its records of outrageous performances [16–18]. Inspired from the foraging behavior of honeybees, ABC was proposed by Karaboga in 2005 [15]. It is basically a type of computational method [16]. Besides showing efficiency, ABC has demonstrated excellent performance in comparison
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