A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems

Reddy, Aala Kalananda Vamsi Krishna; Narayana, Komanapalli Venkata Lakshmi

doi:10.1016/j.asoc.2020.106903

Cited by 40 publications

(17 citation statements)

References 77 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This approach uses the local as well as global optima and thus gives the optimal solution with minimum cost (fitness function) and takes less iteration (Varshney et al 2021 ). WOA demands no added modification parameters to come to an outstanding balance between its exploration and exploitation (Aala Kalananda and Komanapalli, 2021 ). Study findings present that WOA is outstanding to other optimization methods, for instance, PSO, ACO, GA, differential evolution (DE), and gravitational search for solution precision and convergence speed (Chen et al 2020c ; Kaur and Arora, 2018 ; Mohammed et al 2019 , Jahromi et al 2018 ).…”

Section: A Solution Algorithmmentioning

confidence: 99%

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

2023

View full text Add to dashboard Cite

Within an uncertain environment and following carbon trade policies, this study uses the Extended Exergy Accounting (EEA) method for coal supply chains (SCs) in eight of the world's most significant coal consuming countries. The purpose is to improve the sustainability of coal SCs in terms of Joules rather than money while considering economic, environmental, and social aspects. This model is a multi-product economic production quantity (EPQ) with a single-vendor multi-buyer with shortage as a backorder. Within the SC, there are some real constraints, such as inventory turnover ratio, waste disposal to the environment, carbon dioxide emissions, and available budgets for customers. For optimization purposes, three recent metaheuristic algorithms, including Ant Lion Optimizer, Lion Optimization Algorithm, and Whale Optimization Algorithm, are suggested to determine a near-optimum solution to an "exergy fuzzy nonlinear integer-programming (EFNIP)." Moreover, an exact method (GAMS) is employed to validate the results of the suggested algorithms. Additionally, sensitivity analyses with different percentages of exergy parameters, such as capital, labor, and environmental remediation, are done to gain a deeper understanding of sustainability improvement in coal SCs. The results showed that sustainable coal SC in the USA has the lowest fuzzy total exergy, while Poland and China have the highest.

show abstract

Section: A Solution Algorithmmentioning

confidence: 99%

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

2023

View full text Add to dashboard Cite

show abstract

“…WOA was created with the goal of putting 29 mathematical optimization problems and 6 structural design challenges to the test. It has been proved that WOA is able to solve varieties of optimization problem that includes neural network [127], discrete optimization problem [128], both numerical and engineering problem in [129], and so on. However, WOA is only adopted recently for t-way testing strategy in [130] to solve combinatorial optimization problem.…”

Section: Swarm-based Techniquementioning

confidence: 99%

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

2022

View full text Add to dashboard Cite

Metaheuristic algorithm is a very important area of research that continuously improve in solving optimization problems. Nature-inspired is one of the classifications of metaheuristic algorithm that are becoming more popular among researchers for the last decades. Nature-inspired metaheuristic algorithms contributes significantly to tackling many standing complex problems (such as combinatorial tway testing problem) and achieving optimal results. One challenge in this area is combinatorial explosion problem which always intended to find the most optimal final test suite that will cover all combinations of a given interaction strength. As such, test case generation is selected as the most active research area in combinatorial t-way testing as Non-deterministic Polynomial-time hardness (NP-hard). However, not all metaheuristics are effectively adopted in combinatorial t-way testing, some proved to be effective and thus have been popular tools selected for optimization whilst others are not adopted. This research paper outlines hundred and ten (110) outstanding nature-inspired metaheuristic algorithms for the last decades (2001 and 2021) such as Coronavirus Optimization Algorithm, Ebola Optimization Algorithm, Harmony Search, Tiki-Taka Algorithm, and so on. The purpose of this review is to revisit and carry out up-to-date review on these distinguished algorithms with their respective current state of use. This is to inspire future research in the field of combinatorial t-way testing for better optimization. Thus, we found that all metaheuristics has a simple structure to be adopted in different areas for becoming a more efficient in optimization. Finally, we suggested some future paths of investigation for researchers who are interested in the combinatorial t-way testing field to employ more of these algorithms by tuning their parameters setting to achieve an optimal solution.

show abstract

“…The spotted hyena optimizer (SHO) [ 46 ] is a revolutionary metaheuristic approach encouraged by spotted hyenas’ original combined actions in hunting, circling, and attacking prey. The whale optimization approach (WOA) [ 47 ] is a intermix metaheuristics approach that uses whale and swarm human-based optimizers to find optimal exploration and convergence capabilities. MOSHO [ 48 ] is a multi-objective spotted hyena optimizer that lowers several key functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

Kumari

Kamboj

Bath

2022

Complex Intell. Syst.

View full text Add to dashboard Cite

The existing slime mould algorithm clones the uniqueness of the phase of oscillation of slime mould conduct and exhibits slow convergence in local search space due to poor exploitation phase. This research work exhibits to discover the best solution for objective function by commingling slime mould algorithm and simulated annealing algorithm for better variation of parameters and named as hybridized slime mould algorithm–simulated annealing algorithm. The simulated annealing algorithm improves and accelerates the effectiveness of slime mould technique as well as assists to take off from the local optimum. To corroborate the worth and usefulness of the introduced strategy, nonconvex, nonlinear, and typical engineering design difficulties were analyzed for standard benchmarks and interdisciplinary engineering design concerns. The proposed technique version is used to evaluate six, five, five unimodal, multimodal and fixed-dimension benchmark functions, respectively, also including 11 kinds of interdisciplinary engineering design difficulties. The technique’s outcomes were compared to the results of other on-hand optimization methods, and the experimental results show that the suggested approach outperforms the other optimization techniques.

show abstract

A combinatorial social group whale optimization algorithm for numerical and engineering optimization problems

Cited by 40 publications

References 77 publications

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

Contact Info

Product

Resources

About