Author
M. Fesanghary
Other affiliations: Amirkabir University of Technology
Bio: M. Fesanghary is an academic researcher from Louisiana State University. The author has contributed to research in topics: Harmony search & HS algorithm. The author has an hindex of 16, co-authored 25 publications receiving 3451 citations. Previous affiliations of M. Fesanghary include Amirkabir University of Technology.
Papers
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TL;DR: The impacts of constant parameters on harmony search algorithm are discussed and a strategy for tuning these parameters is presented and the proposed algorithm can find better solutions when compared to HS and other heuristic or deterministic methods.
1,782 citations
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TL;DR: Numerical results reveal that the proposed harmony search (HS) algorithm can find better solutions when compared to conventional methods and is an efficient search algorithm for CHPED problem.
405 citations
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TL;DR: In this paper, a hybrid harmony search algorithm (HHSA) is proposed to solve engineering optimization problems with continuous design variables, where sequential quadratic programming (SQP) is employed to speed up local search and improve precision of the HSA solutions.
319 citations
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TL;DR: In this article, a multi-objective optimization model based on harmony search algorithm (HS) is presented to minimize the life cycle cost (LCC) and carbon dioxide equivalent (CO2-eq) emissions of the buildings.
251 citations
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TL;DR: In this paper, the authors explored the use of global sensitivity analysis (GSA) and harmony search algorithm (HSA) for design optimization of shell and tube heat exchangers (STHXs) from the economic viewpoint.
199 citations
Cited by
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TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
10,082 citations
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TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.
7,090 citations
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TL;DR: The qualitative and quantitative results prove the efficiency of SSA and MSSA and demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.
3,027 citations
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TL;DR: The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems and the statistical results show that this algorithm is able to provide very promising and competitive results.
Abstract: In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.
2,892 citations
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TL;DR: This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer, based on three concepts in cosmology: white hole, black hole, and wormhole, which outperforms the best algorithms in the literature on the majority of the test beds.
Abstract: This paper proposes a novel nature-inspired algorithm called Multi-Verse Optimizer (MVO). The main inspirations of this algorithm are based on three concepts in cosmology: white hole, black hole, and wormhole. The mathematical models of these three concepts are developed to perform exploration, exploitation, and local search, respectively. The MVO algorithm is first benchmarked on 19 challenging test problems. It is then applied to five real engineering problems to further confirm its performance. To validate the results, MVO is compared with four well-known algorithms: Grey Wolf Optimizer, Particle Swarm Optimization, Genetic Algorithm, and Gravitational Search Algorithm. The results prove that the proposed algorithm is able to provide very competitive results and outperforms the best algorithms in the literature on the majority of the test beds. The results of the real case studies also demonstrate the potential of MVO in solving real problems with unknown search spaces. Note that the source codes of the proposed MVO algorithm are publicly available at http://www.alimirjalili.com/MVO.html.
1,752 citations