Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Adaptive inertia weight particle swarm optimization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
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The problem of effectively and efficiently finding the global optimum of a function by using evolutionary algorithms is current and pertinent, and two of the evolutionary techniques that have received significant attention in the literature are Particle Swarm Optimization (PSO), and Differential Evolution (DE), as well as their numerous variants. One way of taking advantage of the many good PSO and DE variant algorithms that have appeared in the literature is to run them all for a particular optimization problem and choose the best answer provided. This approach, referred to as the Naive Approach (NA) is time consuming. In this paper, we are using the naive approach with a suite of algorithms for each function minimization problem and we run the algorithms, in the suite, for a specific number of function evaluations (typically much smaller than the number of function evaluations needed for each specific algorithm to converge to a solution) and decide, using appropriate performance measures of merit, which one of these algorithms will continue running until convergence; the rest of the algorithms, deemed as algorithms that will eventually produce inferior solutions, are aborted. We refer to our methodology, for obvious reasons, as the Winner-Take-All (WTA) methodology. Using this methodology we introduce WTA algorithmic variants that are efficient and effective solvers of global optimization problems. In this paper, we report results on one of these variants, called WTA1, and show that it is very competitive compared to the constituent algorithms in the suite used for its design, and efficient compared to NA.