Abstract
Classic algorithms that are based on the ant system theory have been designed to face problems with discrete solutions. When dealing with non discrete problems, in order to apply Ant Colony Optimization (AGO) algorithms they should be transformed to discrete problems. The arbitrary limitation of the number of possible solutions for each space is the result of the transformation of the solutions of the problems from the continuous to the discrete workspace. Hence, the choice of the width of the solutions spaces essentially defines the possible best solutions of the problem. In order to deal with this disadvantage, we present a new algorithm: the Continuous Ant Colony (C-ANT). This algorithm encourages local searching around the best solution found in each iteration. The proposed (C-ANT) is applied to a simple ELD problem composed of 4 generators. Comparison to conventional Particle Swarm Optimization (PSO) algorithms is presented.
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