A novel chaos-integrated symbiotic organisms search algorithm for global optimization

Saha, Subhodip; Mukherjee, V.

doi:10.1007/s00500-017-2597-4

Cited by 48 publications

(21 citation statements)

References 46 publications

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“…For highlighting the contribution of our proposal, obtained numerical results are also compared to those offered by a recent study (Saha and Mukherjee, 2018) that suggests the use of chaotic SOS (CSOS) algorithm, which results in superior performance than the original SOS. Thus, only the numerical values pertaining to CSOS are reported here for comparison purposes.…”

Section: Analytical Benchmark Problemsmentioning

confidence: 99%

“…Chaos is essentially a randomness generated by deterministic systems and its performance is highly dependent on the choice of initial value as it determines the chaotic orbit (Xiang et al, 2007;Saha and Mukherjee, 2018). The chaos theory, with the properties of simplicity, stochasticity and ergodicity, has been a popular and fruitful paradigm integrated into population-based algorithms for improving search capability and evading from local optima stagnation (Xiang et al, 2007;Mukherjee, 2016, 2018;Mirjalili and Gandomi, 2017;Alatas et al, 2009;Gandomi et al, 2012;Coelho and Mariani, 2012;Güvenç et al, 2018).…”

Section: Chaotic Local Searchmentioning

confidence: 99%

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A powerful variant of symbiotic organisms search algorithm for global optimization

Çelik

2020

Engineering Applications of Artificial Intelligence

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This paper suggests a new variation to the existing symbiotic organisms search (SOS) algorithm developed by simulating three symbiotic strategies of mutualism, commensalism and parasitism used by the organisms. In the revised version called improved SOS (ISOS), the theory of quasi-oppositional based learning is employed during generation of initial population and in the parasitism phase to raise the possibility of getting closer to high-quality solutions. An efficient alternative for parasitism phase is also presented. The two upgraded parasitism strategies avoid the over exploration issue of original parasitism phase that causes unwanted longtime search in the inferior search space as the solution is already refined. To guide the algorithm perform an exhaustive search around the best solution in attempting to further improve the search model of ISOS, a chaotic local search based on the piecewise linear chaotic map is coupled into the proposed algorithm. Twentysix benchmark functions and three engineering design problems are tested and a contrast with other popular metaheuristics is widely established. Comparative results substantiate the great contribution of proposed ISOS algorithm in solving various optimization problems with superior global search capability and convergence characteristics which render it useful in handling global optimization problems.

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Section: Analytical Benchmark Problemsmentioning

confidence: 99%

Section: Chaotic Local Searchmentioning

confidence: 99%

A powerful variant of symbiotic organisms search algorithm for global optimization

Çelik

2020

Engineering Applications of Artificial Intelligence

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“…The CEM has a better optimization effect when the search space is small, which can improve the exploitative talents of SSA and effectively avoid running into LO. Literature also recommends that the chaos-based mechanisms can promote the exploratory and exploitative traits of other algorithms [43], [45], [46]. The CEM is represented as follows:…”

Section: A Cem With a ''Shrinking'' Modementioning

confidence: 99%

Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers

et al. 2019

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Salp swarm algorithm (SSA) is a newly developed meta-heuristic algorithm, which is mainly developed based on the swarming behavior of salps sailing and foraging in the ocean. An improved salp swarm-based optimizer is proposed in this paper to overcome the potential shortcomings of original SSA, including being easily trapped in local or deceptive optima and its slow convergence rates in dealing with some high-dimensional and multimodal landscapes. The designed variant is called CMSSA that combines two strategies simultaneously. First, a chaotic exploitative mechanism with ''shrinking'' mode is introduced into the basic SSA to improve the exploitative tendencies of the algorithm. Then, a combined mutation scheme is adapted to make full use of the strong intensification capabilities of Gaussian mutation and the strong exploratory leanings of Cauchy mutation. In addition, the embedded strategies can achieve a more stable equilibrium between the core searching patterns of the SSA, which are diversification and intensification. We thoroughly studied the optimization advantages of the improved CMSSA using several representative benchmark cases, including unimodal, multimodal, and fixed-dimension multimodal functions, and three well-regarded engineering cases. The obtained experimental results, statistical tests, and comparative simulations indicate that the exploratory and exploitative proclivities of the SSA and its convergence patterns are vividly improved. The results indicate that the proposed CMSSA is a promising algorithm and shows superior efficacy compared with other algorithms.

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“…Therefore, researchers have usually considered a weighted sum approach to optimize multiple objectives of DG planning. [41][42][43] Recently, an improved version of SOS, namely, chaotic SOS (CSOS), is proposed by Saha and Mukherjee. Besides the weighted sum approach, there exists another approach to solve MOO problems that simultaneously optimizes all the objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…34 In recent years, SOS algorithm and some of its variants have been widely applied in solving various power engineering optimization problems, such as economic load dispatch, [35][36][37][38] optimal power flow, 39 congestion management, 40 and optimal DG allocation problem. [41][42][43] Recently, an improved version of SOS, namely, chaotic SOS (CSOS), is proposed by Saha and Mukherjee. 41 The advantage of CSOS is that it has only a few control parameters in comparison with other intelligent algorithms and it exhibits better convergence mobility.…”

Section: Introductionmentioning

confidence: 99%

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

Saha

Mukherjee

2019

Int Trans Electr Energ Syst

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Summary This article presents a new method to decide the optimal locations and sizes of distributed generators (DGs) in radial distribution system (RDS). This task is associated with minimization of total voltage deviation and power loss and maximization of voltage stability index of the RDS. To solve this multiobjective DG allocation problem, a novel metaheuristic algorithm, namely, multiobjective chaotic symbiotic organisms search (SOS) (MOCSOS), is proposed in this article. The proposed MOCSOS is an improved version of the traditional SOS, where a chaotic local search technique is incorporated within the traditional SOS framework. The MOCSOS utilizes a fast nondominated sorting strategy to achieve the Pareto solutions. The constraints associated with this DG allocation problem are handled using a binary tournament selection–based approach. The performance of MOCSOS is validated on 33‐ and 69‐node RDSs. The efficacy of the MOCSOS is established by comparing its performance with other nature‐inspired state‐of‐the‐art algorithms.

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A novel chaos-integrated symbiotic organisms search algorithm for global optimization

Cited by 48 publications

References 46 publications

A powerful variant of symbiotic organisms search algorithm for global optimization

A powerful variant of symbiotic organisms search algorithm for global optimization

Chaos-Induced and Mutation-Driven Schemes Boosting Salp Chains-Inspired Optimizers

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

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