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Nature-Inspired Computing and Optimization pp 425–448Cite as

An Improved Spider Monkey Optimization for Solving a Convex Economic Dispatch Problem

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Part of the book series: Modeling and Optimization in Science and Technologies ((MOST,volume 10))

Abstract

Spider monkey optimization (SMO) is a recent population-based swarm intelligence algorithm. It has powerful performance when it applied to solve global optimization problems. In this paper, we propose a new spider monkey optimization algorithm for solving a convex economic dispatch problem. Economic load dispatch (ELD) is a nonlinear global optimization problem for determining the power shared among the generating units to satisfy the generation limit constraints of each unit and minimizing the cost of power production. Although the efficiency of the spider monkey optimization algorithm, it suffers from slow convergence and stagnation when it applied to solve global optimization problems. We proposed a new hybrid algorithm in order to overcome this problem by invoking the multidirectional search method in the final stage of the standard spider monkey optimization algorithm. The proposed algorithm is called multidirectional spider monkey optimization algorithm (MDSMO). The proposed algorithm can accelerate the convergence of the proposed algorithm and avoid trapping in local minima. The general performance of the proposed MDSMO algorithm is tested on a six-generator test system for a total demand of 700 and 800 MW and compared against five Nature-Inspired algorithms. The experimental results show that the proposed algorithm is a promising algorithm for solving economic load dispatch problem.

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Author information

Authors and Affiliations

  1. Faculty of Computers and Informatics, Department of Computer Science, Suez Canal University, Ismailia, Egypt

    Ahmed Fouad Ali

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  1. Ahmed Fouad Ali
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Correspondence to Ahmed Fouad Ali .

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Editors and Affiliations

  1. Dept of Computer Science and Engineering, SOA University Dept of Computer Science and Engineering, Bhubaneswar, Odisha, India

    Srikanta Patnaik

  2. School of Science and Technology, Middlesex University School of Science and Technology, London, United Kingdom

    Xin-She Yang

  3. School of H.S.E., University of Hyogo School of H.S.E., Himeji, Japan

    Kazumi Nakamatsu

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Ali, A.F. (2017). An Improved Spider Monkey Optimization for Solving a Convex Economic Dispatch Problem. In: Patnaik, S., Yang, XS., Nakamatsu, K. (eds) Nature-Inspired Computing and Optimization. Modeling and Optimization in Science and Technologies, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-319-50920-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-50920-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50919-8

  • Online ISBN: 978-3-319-50920-4

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