Randomness as source for inspiring solution search methods: Music based approaches

Abstract

As the world progresses towards industrialization, engineering problems become increasingly complex and it becomes even more difficult to optimize these problems. The reason for this is the increasing complexity of variables, dimensions, space complexity, and time complexity. In order to be able to cope with such a situation, randomized intelligent optimization and search algorithms are proposed to optimize numerical benchmarking problems, multi-objective problems, and solve difficult problems including a large number of variables, dimensions, constraints, and objectives. Metaheuristic random search and optimization methods are widely used to search and find the most appropriate solutions for large-scale optimization problems in an acceptable time. They are general-purposed methods that can be efficiently applied to optimization and search problems without too much modification to accommodate a specific probing. Metaheuristic optimization algorithms are generally categorized as physics, music, sociology, biology, swarm, mathematics, plant, chemistry, sports, water, and hybrid based. Although most of the intelligent metaheuristic methods are inspired by physics and biology: concepts, activities, rules, and processes in music can be an inspiration source of new intelligent optimization and search techniques. That is why; novel and efficient music inspired intelligent optimization and search methods having effective exploitation and exploration capabilities have been proposed. In this paper, music based metaheuristic optimization algorithms were gathered and analyzed for the first time. Harmony search and its versions, melody search algorithm, and method of musical composition have been examined in detail. Furthermore, their performances have been compared within both unconstrained numerical benchmark functions and constrained problems and the obtained results from music based algorithms have been comparatively studied. (C) 2019 Elsevier B.V. All rights reserved.