Systematic Literature Review on Parallel Trajectory-based Metaheuristics

Abstract

In the past 35 years, parallel computing has drawn increasing interest from the academic community, especially in solving complex optimization problems that require large amounts of computational power. The use of parallel (multi-core and distributed) architectures is a natural and effective alternative to speeding up search methods, such as metaheuristics, and to enhancing the quality of the solutions. This survey focuses particularly on studies that adopt high-performance computing techniques to design, implement, and experiment trajectory-based metaheuristics, which pose a great challenge to high-performance computing and represent a large gap in the operations research literature. We outline the contributions from 1987 to the present, and the result is a complete overview of the current state-of-the-art with respect to multi-core and distributed trajectory-based metaheuristics. Basic notions of high-performance computing are introduced, and different taxonomies for multi-core and distributed architectures and metaheuristics are reviewed. A comprehensive list of 127 publications is summarized and classified according to taxonomies and application types. Furthermore, past and future trends are indicated, and open research gaps are identified.