Small-World Particle Swarm Optimizer for Real-World Optimization Problems

  • Megha Vora
  • T. T. Mirnalinee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


Many real-world problems from different domains, viz. engineering, data mining, biology, can be formulated as the optimization of a continuous function. These problems require the estimation of a set of model parameters or state variables that provide the best possible solution to a predefined cost or objective function, or a set of optimal trade-off values in the case of two or more conflicting objectives. Locating global optimal solutions becomes challenging especially in the presence of high dimensionality, nonlinear parameter interaction, insensitivity, and multi-modality of the objective function. These conditions make it very difficult for any search algorithm to find high-quality solutions quickly without getting stuck in local optima. Unfortunately, these difficulties are frequently encountered in real-world optimization problems when traversing the search space en route to the global optimum. Small-world PSO has been proven to be effective in solving global function optimization problems. After all, every optimization algorithm has to be applied to some real-world problems. This paper evaluates the performance of small-world PSO algorithm on two real-world function optimization problems. Comparative study with state of the art demonstrates the effectiveness of small-world PSO.


Small-world PSO Frequency modulation Genetic algorithm with a new multi-parent crossover SLPSO SWPSO-I algorithm 


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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringSSN College of Engineering, Anna UniversityChennaiIndia

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