Abstract
This paper presents a gravitation-based chaos water cycle algorithm for numerical optimization by suitably integrating gravitational search and water cycle algorithm. In new algorithm, the positions of particles are first updated according to gravitational search. To enhance search ability and population diversity, a new chaotic mapping is then defined and incorporated in water cycle algorithm to update the population. Finally, the performance of the proposed algorithm is demonstrated by numerical experiments and comparisons with five widely used algorithms on well-known benchmark functions and a practical problem.
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