Multi-wave algorithms for metaheuristic optimization

We propose new iterated improvement neighborhood search algorithms for metaheuristic optimization by exploiting notions of conditional influence within a strategic oscillation framework. These approaches, which are unified within a class of methods called multi-wave algorithms, offer further refinements by memory based strategies that draw on the concept of persistent attractiveness. Our algorithms provide new forms of both neighborhood search methods and multi-start methods, and are readily embodied within evolutionary algorithms and memetic algorithms by solution combination mechanisms derived from path relinking. These methods can also be used to enhance branching strategies for mixed integer programming.