A Comparative Study Using Shuffled Complex Evolution and Differential Evolution Applied to Robotic Manipulator Design

Brandão, Milena Almeida Leite; Doricio, José Laércio; Lobato, Fran Sérgio; Saramago, Sezimária de Fátima Pereira

doi:10.5151/meceng-wccm2012-18598

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…The SCE algorithm has been successfully applied to a broad range of optimization problems, such as: calibrating rainfall-runoff models (Kuczera, 1997;Franchini et al, 1998;Muttil and Liong, 2004); formulation and solution of a multi-objective global optimization for hydrologic models (Yapo et al, 1998); multi-objective auto-calibration for semi-distributed water quality models (van Griensven and Bauwens, 2003); optimal design ow water distribution network (Liong and Atiquzzaman, 2004), discrete optimization problems by using a hybridization between SCE and Frog Leaping algorithm (Eusuff et al, 2006); calibration of conceptual watershed models (Duan et al, 1994;Tang and Luan, 2007); a comparison between SCE and Differential Evolution (Storn and Price, 1997;Price et al, 2005) to robotic manipulator design (Brandão et al, 2014); hydrologic models (Naeini et al, 2019); solution of reservoir scheduling problems using SCE and Stochastic Ranking procedure (Mao et al, 2019).…”

Section: Multi-objective Optimization Problemmentioning

confidence: 99%

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A novel multi-objective optimization method with local search scheme using shuffled complex evolution applied to mechanical engineering problems

Lobato

Libotte

Platt

2022

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PurposeIn this work, the multi-objective optimization shuffled complex evolution is proposed. The algorithm is based on the extension of shuffled complex evolution, by incorporating two classical operators into the original algorithm: the rank ordering and crowding distance. In order to accelerate the convergence process, a Local Search strategy based on the generation of potential candidates by using Latin Hypercube method is also proposed.Design/methodology/approachThe multi-objective optimization shuffled complex evolution is used to accelerate the convergence process and to reduce the number of objective function evaluations.FindingsIn general, the proposed methodology was able to solve a classical mechanical engineering problem with different characteristics. From a statistical point of view, we demonstrated that differences may exist between the proposed methodology and other evolutionary strategies concerning two different metrics (convergence and diversity), for a class of benchmark functions (ZDT functions).Originality/valueThe development of a new numerical method to solve multi-objective optimization problems is the major contribution.

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Section: Multi-objective Optimization Problemmentioning

confidence: 99%

“…, 1994; Tang and Luan, 2007); a comparison between SCE and Differential Evolution (Storn and Price, 1997; Price et al. , 2005) to robotic manipulator design (Brandão et al. , 2014); hydrologic models (Naeini et al.…”

Section: Shuffled Complex Evolution Its Extension For Multi-objective...mentioning

confidence: 99%

A novel multi-objective optimization method with local search scheme using shuffled complex evolution applied to mechanical engineering problems

Lobato

Libotte

Platt

2022

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“…In [6], a simplified scheme for the optimal design of the robot manipulator is solved by the Differential Evolution and Shuffled Complex Evolution optimization methods.…”

Section: Numerical Simulationmentioning

confidence: 99%

Optimum design of 3R robot manipulador by using Improved Differential Evolution implemented in parallel computation

Brandão

Fátima

Saramago

et al. 2020

Braz. Elect. J. Math.

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In recent decades the great interest in Evolutionary Algorithms (EAs) has boosted their development leading to a significant improvement in their efficiency and applicability. Thus, EAs have been applied to solve optimization problems in different areas of knowledge. A promising optimization method known as Differential Evolution (DE), which belongs to the class of AEs, has attracted the attention of researchers. The DE algorithm is simple, robust and efficient. However, by testing with classical optimization problems noticed that sometimes the results obtained with DE are not as satisfactory as expected or that in many cases the algorithm ends the search for the optimal solution prematurely. Recently, with the advancement and greater availability of computer technology, the scientific community has been thinking about the implementation of optimization algorithms in parallel in order to reduce the processing time. The main objective of this paper is to present an improvement of the Differential Evolution optimization method, proposing modifications to the basic algorithm by using shuffled complex and making it able to work with parallel computing. The proposed methodology is applied to the optimal design of an orthogonal 3R robot manipulator that takes into account the characteristics of its workspace. For this purpose, a multi-objective optimization problem is formulated to obtain the optimal geometric parameters for the robot. The maximum workspace volume, the maximum system stiffness and the optimum dexterity are considered as the multi-objective functions. The results show that the procedure represents a promising alternative for the type of problem presented above.

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“…É importante mencionar que parte dos resultados expostos nas Tabs. 5.12 e 5.13 foram apresentados por BRANDÃO et al (2012) no World Congress on Computational Mechanics.…”

Section: Simulação Numérica Do Problema Irrestritounclassified

Evolução diferencial melhorada implementada em processamento paralelo

Brandão¹

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RESUMOO objetivo deste trabalho é apresentar um aprimoramento do método de otimização heurístico Evolução Diferencial propondo modificações no seu algoritmo básico através da utilização do conceito de Evolução com Conjuntos Embaralhados. Desenvolveu-se o método denominado Evolução Diferencial Melhorada (EDM), sendo este algoritmo implementado em computação paralela (EDMP), tornando-o apto para solucionar problemas de otimização complexos. O algoritmo desenvolvido é adaptado para trabalhar com problemas de otimização multiobjetivos e na presença de restrições. Algumas funções testes são resolvidas usando o método EDMP a fim de validar o algoritmo. A metodologia EDMP é utilizada para obter o projeto ótimo de um robô manipulador com três juntas rotacionais (3R) levando-se em conta as características de sua topologia. Com esta finalidade, um problema de otimização multiobjetivo é formulado para a obtenção dos parâmetros geométricos ótimos do robô, considerando a maximização do volume do espaço de trabalho, de sua rigidez e a otimização de sua destreza. Finalmente, o algoritmo EDMP é aplicado na solução de grandes sistemas lineares, reescritos como um problema de minimização de resíduos. Todos os resultados obtidos com o algoritmo desenvolvido são comparados com as soluções calculadas por meio de outras metodologias, a fim de comprovar sua eficiência e o relevante ganho em termos de tempo computacional.

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A Comparative Study Using Shuffled Complex Evolution and Differential Evolution Applied to Robotic Manipulator Design

Abstract: Abstract. In last decades, evolutionary approaches has been used extensively and demons-

Cited by 3 publications

References 27 publications

A novel multi-objective optimization method with local search scheme using shuffled complex evolution applied to mechanical engineering problems

A novel multi-objective optimization method with local search scheme using shuffled complex evolution applied to mechanical engineering problems

Optimum design of 3R robot manipulador by using Improved Differential Evolution implemented in parallel computation

Evolução diferencial melhorada implementada em processamento paralelo

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