# Multi-wave algorithms for metaheuristic optimization

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## Abstract

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.

## Keywords

Metaheuristic optimization Iterated neighborhood search Multi-start algorithms Tabu search Evolutionary algorithms Mixed integer programming## Notes

### Acknowledgments

I am indebted to Raca Todosijević for his help in preparing the diagrams for the algorithms in this paper, and also owe my gratitude to two reviewers whose comments have helped to improve the paper’s exposition.

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