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HMS-OS :
HMS-OS : Improving the Human Mental Search Optimisation Algorithm by Grouping in both Search and Objective Space
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- Mousavirad, S. J. (author)
- Hakim Sabzevari University, Computer Engineering Department, Sabzevar, Iran
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- Schaefer, G. (author)
- Loughborough University, Department of Computer Science, Loughborough, United Kingdom
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- Korovin, I. (author)
- Southern Federal University, Taganrog, Russian Federation
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- Oliva, D. (author)
- Universidad de Guadalajara, Depto. de Ciencias Computacionales, Guadalajara, Mexico
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- Helali Moghadam, Mahshid (author)
- Mälardalens universitet,RISE Research Institutes of Sweden, Sweden
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- Saadatmand, Mehrdad, 1980- (author)
- RISE Research Institutes of Sweden
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2021
- 2021
- English.
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In: 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728190488
- Related links:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- The human mental search (HMS) algorithm is a relatively recent population-based metaheuristic algorithm, which has shown competitive performance in solving complex optimisation problems. It is based on three main operators: mental search, grouping, and movement. In the original HMS algorithm, a clustering algorithm is used to group the current population in order to identify a promising region in search space, while candidate solutions then move towards the best candidate solution in the promising region. In this paper, we propose a novel HMS algorithm, HMS-OS, which is based on clustering in both objective and search space, where clustering in objective space finds a set of best candidate solutions whose centroid is then also used in updating the population. For further improvement, HMS-OS benefits from an adaptive selection of the number of mental processes in the mental search operator. Experimental results on CEC-2017 benchmark functions with dimensionalities of 50 and 100, and in comparison to other optimisation algorithms, indicate that HMS-OS yields excellent performance, superior to those of other methods.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Clustering
- Human mental search
- Metaheuristics
- Objective space
- Optimisation
- Benchmarking
- Clustering algorithms
- Clusterings
- Competitive performance
- Complex optimization problems
- Meta-heuristics algorithms
- Metaheuristic
- Optimisations
- Search Algorithms
- Search spaces
- Optimization
Publication and Content Type
- ref (subject category)
- kon (subject category)
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