An Improved Spider Monkey Optimization for Solving a Convex Economic Dispatch Problem
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...
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          | Published in | Nature-Inspired Computing and Optimization Vol. 10; pp. 425 - 448 | 
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| Main Author | |
| Format | Book Chapter | 
| Language | English | 
| Published | 
        Switzerland
          Springer International Publishing AG
    
        2017
     Springer International Publishing  | 
| Series | Modeling and Optimization in Science and Technologies | 
| Subjects | |
| Online Access | Get full text | 
| ISBN | 9783319509198 3319509195  | 
| ISSN | 2196-7326 2196-7334  | 
| DOI | 10.1007/978-3-319-50920-4_17 | 
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| Summary: | 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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| ISBN: | 9783319509198 3319509195  | 
| ISSN: | 2196-7326 2196-7334  | 
| DOI: | 10.1007/978-3-319-50920-4_17 |