Particle Swarm Optimization Essay

4198 Words Sep 24th, 2015 17 Pages
Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search
Kaushik Suresh, Sayan Ghosh, Debarati Kundu, Abhirup Sen, Swagatam Das and
*
Ajith Abraham
1

Department of Electronics and Telecommunication Engineering
Jadavpur University, Kolkata, India
*
Center of Excellence for Quantifiable Quality of Service,
Norwegian University of Science and Technology, Trondheim, Norway ajith.abraham@ieee.org Abstract
This paper describes a method for improving the final accuracy and the convergence speed of Particle
Swarm Optimization (PSO) by adapting its inertia factor in the velocity updating equation and also by adding a new coefficient to the position updating equation. These modifications do not impose any
serious
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PSO is however not free from false and/or premature convergence, especially over multimodal fitness landscapes. In this article, we describe a new variant of the basic PSO, which improves the

performance of the algorithm in two ways. Firstly, the inertia factor of the classical PSO has been adapted in such a fashion that whenever a particle moves far away from the globally best position found so far by the swarm, the effect of its inertial velocity will be minimal. Secondly, a momentum factor has been added to the position updating equation of the classical
PSO, which gives greater mobility to the particles even when their velocities become very low due to false convergence to some local minima.

2. The Particle Swarm Optimizers
2.1 The Classical PSO
PSO is in principle, a multi-agent parallel search technique and bears many common features with other population based optimization techniques, such as the
Genetic Algorithms (GAs) [3]. PSO starts with the random initialization of a population of candidate solutions (particles) over the fitness landscape.
However, unlike other evolutionary computing techniques, PSO uses no direct recombination of genetic material between individuals during the search. Rather it works depending on the social behavior of the particles in the swarm. Therefore, it finds the global best solution by simply

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