Particle swarm optimisation

Goudos, S.K. and Sahalos, J.N., Microwave absorber optimal design using multiobjective particle swarm optimisation, Microwave Optic. Tech. Letts., 48, 1553, 2006. 

Particle swarm optimisation is a population-based stochastic optimisation technique (Eberhart and Kennedy, 1995),... 

Key words drilling burr size Box-Behnken design of experiments response surface methodology particle swarm optimisation. 

The particle swarm optimisation (PSO) was developed by Kennedy and Eberhart, which is a relatively recent... 

The application of particle swarm optimisation (PSO) for simultaneous minimisation of burr height and burr thickness during drilUng ofAlS1316L stainless steel has been presented in this chapter. The machining tests were performed with a... 

During the last decade two novel stochastic optimisation methods came into use which, like evolutionary algorithms, are based on heuristics inspired by nature. These methods are particle swarm optimisation (Kennedy and Eberhart, 2001), and ant colony optimisation (Dorigo and... 

Poll, R. Analysis of the publications on the applications of Particle Swarm Optimisation. J. Artif. Evol. Appl. 2008, 1-10 (2008)... 

Clerc, M. Standard particle swarm optimisation from 2006 to 2011. Technical report. Particle Swarm Central (2011)... 

A problem is given, and a way to evaluate a proposed solution to it exists in the form of a fitness function. Then a population of individuals made by random guesses to the problem solution is initialised. These individuals are candidate solutions, also known as the particles, hence the name particle swarm. Each particle has a very simple memory of its personal best solution so far, called p The global best solution for each iteration is also found and labelled g est - It is the best value, obtained so far by any particle in the population. Each particle makes this information available to their neighbours, analogous to social interaction. Once set in motion each particle is moved a certain distance from its current location, influenced by a random amount by the p beg/ and g test values to improve fimess. During this iterative process, the particles gradually settle down to an optimum solution. In a minimisation optimisation problem, problems are formulated so that best simply means the position with the smallest objective value. 

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