Chaos based optics inspired optimization algorithms as global solution search approach

Bingol, Harun; Alataş, Bilal

doi:10.1016/j.chaos.2020.110434

Cited by 66 publications

(40 citation statements)

References 29 publications

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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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Section: Literature Reviewmentioning

confidence: 99%

Single candidate optimizer: a novel optimization algorithm

et al. 2022

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“…where n is the number of dimensions. A chaos number cs i in the related dimension consists of centre point (cp) and radius (r) as shown in Equation (11). 12) and ( 13), respectively.…”

Section: Novel Encoding Type For Evolutionary Optimizationmentioning

confidence: 99%

“…Therefore, chaos has many advantages and has been widely used in metaheuristic optimization methods in recent years. [9][10][11][12][13] Chaos-based a novel meaning for numbers was proposed in Reference 14. Chaos numbers are not rigid and not static.…”

Section: Introductionmentioning

confidence: 99%

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Altay

Alataş

2021

Concurrency and Computation

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In some practical situations, new computational methods are required for appropriately representing systems and their variables with inaccuracies, uncertainties, or variability. The chaos numbers seem to efficiently represent a set or range of values with lower and upper bounds for variables. In this article, a new chaos-enhanced representation scheme that is based on the notion of chaos numbers is proposed for evolutionary optimization methods. As a first application of chaos-based new encoding type, it is integrated into the novel hybrid intelligent optimization method proposed that is adapted as numerical association rules miner for the first time. The proposed hybrid method can also be used for complex types of search and optimization problems.The method is designed as a multiobjective rule miner that simultaneously handles different conflicting objectives and finds the accurate and comprehensible rules automatically. Based on the chaotic encoding, the proposed method easily and effectively adjusts the intervals of the attributes and automatically mines the rules without any preprocess. The performance of the proposed method was tested in real quantitative data sets and results were compared with the other association rule mining methods.According to the obtained results, the proposed method seems promising with respect to different metrics.

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“…The use of gravitational force in a system consisting of masses at different distances from each other has been a fundamental inspiration in introducing the GSA. Some of other physics-based metaheuristics are: the Water Cycle Algorithm (WCA) [26], Optics Inspired Optimization (OIO) [27,28], the Central Force Optimization (CFO) [29], the Nuclear Reaction Optimization (NRO) [30], the Multi-Verse Optimizer (MVO) [31], and the Atom Search Optimization (ASO) [32].…”

Section: Introductionmentioning

confidence: 99%

Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm

2022

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This paper proposes a new nature-based metaheuristic algorithm called Fennec Fox Optimization (FFA), mimicking two natural behaviors of the animal Fennec Fox in nature. Concretely, Fennec's digging ability and escape strategy from wild predators were the fundamental inspiration for the proposed FFA. The mathematical model of FFA is presented in two phases based on imitating these two behaviors. First, the efficiency of FFA was evaluated in the optimization of sixty-eight standard benchmark functions and four engineering design problems. Second, FFA performance is challenged against eight wellknown optimization algorithms. The optimization results show that FFA perfectly balances exploration and exploitation in searching for the global optimum. Hence, FFA can provide suitable solutions to optimization problems. The comparison of results indicates the superiority of FFA in most objective functions over competitor algorithms in providing the optimal solution.

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Chaos based optics inspired optimization algorithms as global solution search approach

Cited by 66 publications

References 29 publications

Single candidate optimizer: a novel optimization algorithm

Single candidate optimizer: a novel optimization algorithm

Chaos numbers based a new representation scheme for evolutionary computation: Applications in evolutionary association rule mining

Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm

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