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

Elif Varol Altay and Bilal Alatas

Physica A: Statistical Mechanics and its Applications, 2020, vol. 537, issue C

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.

Keywords: Randomized algorithms; Global optimization; Music based optimization algorithms; Constrained G-suite functions; Benchmark functions (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119315134

DOI: 10.1016/j.physa.2019.122650

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