Adaptive firefly algorithm with chaos for mechanical design optimization problems

Baykasoğlu, Adil; Özsoydan, Fehmi Burçin

doi:10.1016/j.asoc.2015.06.056

Cited by 205 publications

(67 citation statements)

References 57 publications

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“…It therefore makes sense to compare to other results previously published. The results compared to are (1) Coello Coello (2000); (2) Mezura- (2007); (5) Kim et al (2010); (6) Akay and Karaboga (2012); (7) Brajevic and Tuba (2013); (8) Sadollah et al (2013); (9) Gandomi (2014); (10) Baykasoglu and Ozsoydan (2015); (11) Salimi (2015). These represent a spread in the most competitive results obtained for these benchmarks using both historically successful algorithms and more recent state-of-the-art algorithms.…”

Section: Comparison To Previously Published Resultsmentioning

confidence: 99%

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

2016

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A generic constraint handling framework for use with any swarm-based optimization algorithm is presented. For swarm optimizers to solve constrained optimization problems effectively modifications have to be made to the optimizers to handle the constraints, however, these constraint handling frameworks are often not universally applicable to all swarm algorithms. A constraint handling framework is therefore presented in this paper that is compatible with any swarm optimizer, such that a user can wrap it around a chosen swarm algorithm and perform constrained optimization. The method, called separation-sub-swarm, works by dividing the population based on the feasibility of individual agents. This allows all feasible agents to move by existing swarm optimizer algorithms, hence promoting good performance and convergence characteristics of individual swarm algorithms. The framework is tested on a suite of analytical test function and a number of engineering benchmark problems, and compared to other generic constraint handling frameworks using four different swarm optimizers; particle swarm, gravitational search, a hybrid algorithm and differential evolution. It is shown that the new framework produces superior results compared to the established frameworks for all four swarm algorithms tested. Finally, the framework is applied to an aerodynamic shape optimization design problem where a shock-free solution is obtained.

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Section: Comparison To Previously Published Resultsmentioning

confidence: 99%

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

2016

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“…As shown by several works in the literature, integrating chaotic behaviors into population-based optimization approaches allows to substantially improve the exploration and exploitation properties of search agents [21][22][23][24]. With that being said, many recently proposed chaos-based optimization approaches have been successfully applied to solve a wide variety of complex and challenging engineering problems [24].…”

Section: Introductionmentioning

confidence: 99%

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

et al. 2017

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Solar energy is used worldwide to alleviate the daily increasing demands for electric power. Photovoltaic (PV) cells, which are used to convert solar energy into electricity, can be represented as equivalent circuit models, in which a series of electrical parameters must be identified in order to determine their operating characteristics under different test conditions. Intelligent approaches, like those based in population-based optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Simulated Annealing (SA), have been demonstrated to be powerful methods for the accurate identification of such parameters. Recently, chaos theory have been highlighted as a promising alternative to increase the performance of such approaches; as a result, several chaos-based optimization methods have been devised to solve many different and complex engineering problems. In this paper, the Chaotic Gravitational Search Algorithm (CGSA) is proposed to solve the problem of accurate PV cell parameter estimation. To prove the feasibility of the proposed approach, a series of comparative experiments against other similar parameters extraction methods were performed. As shown by our experimental results, our proposed approach outperforms all other methods compared in this work, and proves to be an excellent alternative to tackle the challenging problem of solar cell parameters identification.

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“…During last few years, many scientists and researchers have used several types of nature-inspired metaheuristics to locate the best optimal results of the Welded Beam Design (WBD) problem in the literature, such as Genetic Algorithm (GA) [50][51][52], Unified Particle Swarm Optimization (UPSO) [53], Artificial Bee Colony algorithm (ABC) [54], Co-evolutionary Differential Evolution (CDE) [55], Co-evolutionary Particle Swarm Optimization (CPSO) [56], Harmony Search algorithm (IHS) [57], Moth-Flame Optimization algorithm (MFO) [33], Adaptive Firefly Algorithm (AFA) [58], Charged System Search (CSS) [59] and Lightning Search Algorithm-Simplex Method (LSA-SM) [49].…”

Section: Welded Beam Designmentioning

confidence: 99%

“…The main objective is to minimize the total cost. The pressure vessel design problem can be mathematically formulated as below [49]: During the last few decades, several researchers have used different types of metaheuristics to find the best possible optimal solutions of the Pressure Vessel Design Problem in the literature such as Genetic Algorithm (GA) [50][51][52], Artificial Bee Colony algorithm (ABC) [54], Co-evolutionary Differential Evolution (CDE) [55], Co-evolutionary Particle Swarm Optimization (CPSO) [56], Improved Harmony Search algorithm (IHS) [57], Moth-Flame Optimization algorithm (MFO) [33], Adaptive Firefly Algorithm (AFA) [58], Bat Algorithm (BA) [60], Cuckoo Search algorithm (CS) [61], Evolution Strategies (ES) [62], Ant Colony Optimization (ACO) [63], Teaching-Learning-Based Optimization (TLBO) [64] and Lightning Search Algorithm-Simplex Method (LSA-SM) [49].…”

Section: Pressure Vessel Designmentioning

confidence: 99%

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

Singh

Hachimi

2018

MCA

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Abstract:The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

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Adaptive firefly algorithm with chaos for mechanical design optimization problems

Cited by 205 publications

References 57 publications

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

A generic framework for handling constraints with agent-based optimization algorithms and application to aerodynamic design

A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

A New Hybrid Whale Optimizer Algorithm with Mean Strategy of Grey Wolf Optimizer for Global Optimization

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