A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information. The basic principle of metaheuristics is to sample a set of solutions which is large enough to be completely sampled. As metaheuristics make few assumptions about the optimization problem to be solved, they may be put to use in a variety of problems. Metaheuristics do not however, guarantee that a globally optimal solution can be found on some class of problems since most of them implement some form of stochastic optimization. Hence the solution found is often dependent on the set of random variables generated. By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than optimization algorithms, iterative methods, or simple heuristics. As such, they are useful approaches for optimization problems.
Several books have been published in this direction of image analysis using metaheuristic approaches. Most of them have only dealt with the subject matter using the traditional metaheuristic techniques. But none of them addresses the research issues using hybrid intelligent approaches. The proposed book is targeted to bring forward the latest research in image analysis using the hybrid intelligent metaheuristic paradigms. The use of the hybridization of the metaheuristic approaches will definitely carve out robust and fail-safe solutions for future generation image processing systems. It will entice the readers to develop as well as foresee suitable solutions for the ever evolving problems in image analysis and understanding.