Clustering cuckoo search optimization for economic load dispatch problem
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Clustering cuckoo search optimization for economic load dispatch problem Jiangtao Yu1,2 • Chang-Hwan Kim2 • Sang-Bong Rhee2 Received: 14 January 2020 / Accepted: 12 May 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In this paper, a clustering cuckoo search optimization (CCSO) is proposed. Different from the randomly generated step size in CSO, the step size in CCSO is generated by a clustering mechanism, and the value is updated according to the average fitness value difference between each cluster and the whole swarm, thereby improving the searching balance between exploration and exploitation of each solution. The effectiveness of CCSO has been validated by six typical benchmark functions and economic load dispatch problems with 6, 10, 13, 15 and 40 generators. The results of CSO and CCSO are displayed and compared in aspects of convergence rate, objective function value and robustness. Moreover, the influences of parameters as step size d, solution number P, egg abandon fraction pa and cluster number K are all analyzed comprehensively in this study. The conclusion is that, in all the tested cases, CCSO behaves much more competitive than CSO under the same parameter setting conditions. Keywords Cuckoo search algorithm Cluster Real-world optimization Power system
1 Introduction Economic load dispatch (ELD) in power system is a realworld optimization problem with high dimensional, nonconvex, non-smooth and non-linear characteristics. Since the power demand is rising day by day, the amount of fuel cost is escalating hugely, so it becomes mandatory to decrease the fuel cost and to maintain stable operation of the power system. Meanwhile, with the rapid development of heuristic optimization methods, a number of heuristic optimization methods are focusing on searching for global solution for ELD problems by applying exploration and exploitation procedures. These methods include genetic algorithm (GA) [1, 2], differential evolution (DE) [3–5], particle swarm optimization (PSO) [6, 7], harmony search (HS) [8, 9], grey wolf optimizer (GWO) [10, 11], teaching& Sang-Bong Rhee [email protected] 1
Department of Electronic Information and Electrical Engineering, Anyang Institute of Technology, Anyang 455000, China
2
Department of Electrical Engineering, Yeungnam University, Kyonsan, Gyeongsan-si 38541, Republic of Korea
learning-based optimization (TLBO) [12], mine blast algorithm (MBA) [13], biogeography-based optimization (BBO) [14–16], artificial bee colony (ABC) [17], shuffle frog leaping algorithm (SFLA) [18], Jaya algorithm [19, 20], invasive weed optimization (IWO) [21], etc. However, even though these heuristic methods are simple to use, they are inefficient in solving complex problems, such as the ELD problem with high dimensional, non-convex, non-smooth and non-linear characteristics. Over the latest years, various of hybrid meta-heuristic methods with modifications of original methods are developed to further address this problem
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