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—Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However , unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled(More)
— Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and real-world problems. DE, however, is not completely free(More)
This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images without using the conventional Hough transform methods. The proposed algorithm is based on a recently developed swarm intelligence technique, known as the bacterial foraging optimization (BFO). A new objective function has been derived to(More)
Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. In this paper we present two new, improved variants of DE. Performance comparisons of the two proposed methods are provided against (a) the original DE, (b) the canonical particle swarm optimization (PSO), and (c) two PSO-variants. The(More)
The theoretical analysis of evolutionary algorithms is believed to be very important for understanding their internal search mechanism and thus to develop more efficient algorithms. This paper presents a simple mathematical analysis of the explorative search behavior of a recently developed metaheuristic algorithm called harmony search (HS). HS is a(More)
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of(More)
Hardware/software partitioning is a crucial problem in embedded system design. In this paper, we provide an alternative approach to solve this problem using Particle Swarm Optimization (PSO) algorithm. Performance analysis of the proposed scheme with Integer Linear Programming, Genetic Algorithm and Ant Colony Optimization technique has been compared using(More)