A Physics Based Novel Approach for Travelling Tournament Problem: Optics Inspired Optimization

Alataş, Bilal; Bingol, Harun

doi:10.5755/j01.itc.48.3.20627

Cited by 47 publications

(29 citation statements)

References 22 publications

(32 reference statements)

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“…The key idea of adopting law of reflection in OIO is the concave reflecting surface and convex surface that causes the incident light rays to converge and reflect away respectively, so that they all appear to be diverging. However, OIO algorithm is applied in quite number of areas for optimization, these are travelling tournament problem [288] and truss optimum design problems [289].…”

Section: Physic-based Techniquementioning

confidence: 99%

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

2022

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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.

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Section: Physic-based Techniquementioning

confidence: 99%

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

2022

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“…Another classification of meta-heuristic algorithms is presented in [59] where meta-heuristic algorithms are divided into nine different categories: swarm-based, chemical-based, biology-based, physics-based, sportsbased, musical-based, social-based, mathematical-based, and hybrid approaches. Besides the nine aforementioned categories, the authors in [60][61][62] added water-based, light-based and plant-based as three different classes of intelligent optimization algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Single candidate optimizer: a novel optimization algorithm

et al. 2022

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Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel optimization algorithm, called Single Candidate Optimizer (SCO), that relies only on a single candidate solution throughout the whole optimization process. The proposed algorithm implements a unique set of equations to effectively update the position of the candidate solution. To balance exploration and exploitation, SCO is integrated with the two-phase strategy where the candidate solution updates its position differently in each phase. The effectiveness of the proposed approach is validated by testing it on thirty three classical benchmarking functions and four real-world engineering problems. SCO is compared with three well-known optimization algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, and Gravitational Search Algorithm and with four recent high-performance algorithms: Equilibrium Optimizer, Archimedes Optimization Algorithm, Mayfly Algorithm, and Salp Swarm Algorithm. According to Friedman and Wilcoxon rank-sum tests, SCO can significantly outperform all other algorithms for the majority of the investigated problems. The results achieved by SCO motivates the design and development of new single-solution-based optimization algorithms to further improve the performance. The source code of SCO is publicly available at: https://uk.mathworks.com/matlabcentral/fileexchange/116100-single-candidate-optimizer.

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“…Combinations can also be considered as hybrid category. Genetic algorithm, ant colony algorithms and differential evolution algorithm are biologically based models [37]. Swarm intelligence (SI) falls under the category of collective behavior of organisms found in nature that is of interest to researchers [38].…”

Section: Chameleon Swarm Algorithmmentioning

confidence: 99%

Chameleon Swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

Uluslu

2022

IEEE Access

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Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

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A Physics Based Novel Approach for Travelling Tournament Problem: Optics Inspired Optimization

Cited by 47 publications

References 22 publications

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

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

Single candidate optimizer: a novel optimization algorithm

Chameleon Swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

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