A novel meta-heuristic optimization method based on golden ratio in nature

Nematollahi, Amin Foroughi; Rahiminejad, Abolfazl; Vahidi, B.

doi:10.1007/s00500-019-03949-w

Cited by 97 publications

(78 citation statements)

References 85 publications

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“…Experiments are conducted on an Intel Core i5, 8 GB RAM, and Windows 10 environment. For the LAPO, results are taken from [79], and for the GROM algorithm, the results are taken from [80].…”

Section: Methodsmentioning

confidence: 99%

“…The basic of HHO is in [31], and basic of VPL is in [60]. SELO, LAPO, and GROM can be followed in [61,79,80], respectively.…”

Section: Ssa Goa Lapo Grom Boa Ssoa Vpl Hho and Selo Algorithmsmentioning

confidence: 99%

“…Some popular algorithms are simulated annealing (SA) [62], gravitational local search (GLSA) [63], big-bang big-crunch (BBBC) [64], gravitational search algorithm (GSA) [65], charged system search (CSS) [66], central force optimization (CFO) [67], artificial chemical reaction optimization algorithm (ACROA) [68], black hole (BH) algorithm [69], ray optimization (RO) [70] algorithm, small-world optimization algorithm (SWOA) [71], galaxy-based-search algorithm (GbSA) [72], curved space optimization (CSO) [73], water cycle algorithm(WCA) [74]. Spiral optimization (SO) [75], river formation dynamics (RFD) [76], sine cosine algorithm (SCA) [77], multi verse optimizer (MVO) [78], lightning attachment procedure optimization (LAPO) [79], golden ratio optimization method (GROM) [80].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative study of social group optimization with a few recent optimization algorithms

Naik

Satapathy

2020

Complex Intell. Syst.

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From the past few decades, the popularity of meta-heuristic optimization algorithms is growing compared to deterministic search optimization algorithms in solving global optimization problems. This has led to the development of several optimization algorithms to solve complex optimization problems. But none of the algorithms can solve all optimization problems equally well. As a result, the researchers focus on either improving exiting meta-heuristic optimization algorithms or introducing new algorithms. The social group optimization (SGO) Algorithm is a meta-heuristic optimization algorithm that was proposed in the year 2016 for solving global optimization problems. In the literature, SGO is shown to perform well as compared to other optimization algorithms. This paper attempts to compare the performance of the SGO algorithm with other optimization algorithms proposed between 2017 and 2019. These algorithms are tested through several experiments, including multiple classical benchmark functions, CEC special session functions, and six classical engineering problems etc. Optimization results prove that the SGO algorithm is extremely competitive as compared to other algorithms.

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Section: Methodsmentioning

confidence: 99%

“…The basic of HHO is in [31], and basic of VPL is in [60]. SELO, LAPO, and GROM can be followed in [61,79,80], respectively.…”

Section: Ssa Goa Lapo Grom Boa Ssoa Vpl Hho and Selo Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A comparative study of social group optimization with a few recent optimization algorithms

Naik

Satapathy

2020

Complex Intell. Syst.

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“…An extensive technical background based on meta-heuristics has been presented in the literature in recent years, becoming from the most diverse inspirations, such as the well-known evolutionary algorithms of genetic inspiration [ 23 ], the behavior of students in the classroom [ 24 ], the behavior of different species of animals [ 25 ], and some theoretic-mathematical concepts like the golden ratio [ 26 ]. In particular, some of these techniques have been used successfully in the JSSP solution.…”

Section: Related Workmentioning

confidence: 99%

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Viana

Morandin

Contreras

2020

Sensors

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It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

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“…Meta-heuristic algorithms can be divided into different categories based on varied criteria: single solution based and population based [15], nature inspired and non-nature inspired [16], metaphor based and non-metaphor based [17]. From the 'inspiration' point of view, these algorithms can roughly be divided into four categories [18]: Evolutionary, Swarm inspired, Physics based, and Human related.…”

Section: Related Workmentioning

confidence: 99%

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

Guha¹,

Ghosh²,

Ghosh³

et al. 2020

Preprint

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A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.

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A novel meta-heuristic optimization method based on golden ratio in nature

Cited by 97 publications

References 85 publications

A comparative study of social group optimization with a few recent optimization algorithms

A comparative study of social group optimization with a few recent optimization algorithms

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

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