In search of the essential binary discrete particle swarm

The particle swarm optimization algorithm is an innovative and competitive optimization technique in evolutionary computation. It has been found to be extremely effective in solving a wide range of problems with real-parameter representation; however, it is of low efficiency in dealing with the discrete problems. In this paper, the particle swarm algorithm is broken down into its essential components, and alternative interpretations of those components are proposed. It is simpler and more powerful than the algorithms available. Experimental results show that this algorithm is faster than the standard binary discrete PSO on two suites of test functions, and that accuracy is improved for most benchmark functions used. One suite concerns about binary encoding problems, the other is about continuous-valued functions. A queen informant is also introduced. It does not increase the number of function evaluations; however, it appears it greatly speeds up the convergence.