A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems

Mezura-Montes, Efrén; Coello, Carlos A. Coello

doi:10.1109/tevc.2004.836819

Cited by 531 publications

(167 citation statements)

References 21 publications

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“…These comprised four structural design applications, involving the Welded Beam Design (WBD), the Tension/Compression Spring Design (TSD), the Pressure Vessel Design (PVD) and the Speed Reducer Design (SRD) [1]. Several authors in [1,15,[95][96][97][98] have solved these problems using different methods such as the hybrid PSO-GA algorithm, the artificial bee colony, etc. All four of these selected constrained engineering problems have been considered to be computationally-expensive black-box problems.…”

Section: Further Tests Using Nonlinear Constrained Engineering Applicmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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Section: Further Tests Using Nonlinear Constrained Engineering Applicmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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“…The best result for g05 reported in [14] is better than known optimum as a consequence of larger equality constraint violation. For g01, g03, g04, g06, g08, g11 and g12 problems, GI-ABC [23], improved stochastic ranking (ISR) [24] and over-penalized approach (OPA) [24], adaptive segregational constraint handling evolutionary algorithm (ASCHEA) by Hamida and Schoenaeur [25], genetic algorithm (GA) from [26], simple multimembered evolutional strategy (SMES) by Mezura-Montes and Coello Coello [26], particle swarm optimization (PSO) from [27], and differential evolution (DE).…”

Section: Comparison With the Latest Abc And Other State-of-the-art Almentioning

confidence: 99%

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Bačanin¹,

Tuba²

2012

SIC

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Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was successfully applied to unconstrained optimization problems and later it was adjusted for constrained problems as well. In this paper we introduce modifications to the ABC algorithm for constrained optimization problems that improve performance of the algorithm. Modifications are based on genetic algorithm (GA) operators and are applied to the creation of new candidate solutions. We implemented our modified algorithm and tested it on 13 standard benchmark functions. The results were compared to the results of the latest (2011) Karaboga and Akay's ABC algorithm and other state-of-the-art algorithms where our modified algorithm showed improved performance considering best solutions and even more considering mean solutions.

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“…The experimental design is as follows: 13 test problems taken from the specialized literature [17] were used in this empirical comparison. This benchmark has different features (linear or nonlinear objective function, linear or nonlinear constraints which can be equality or inequality, dimensionality), see Table I and [17] for details.…”

Section: A Experimental Designmentioning

confidence: 99%

“…This benchmark has different features (linear or nonlinear objective function, linear or nonlinear constraints which can be equality or inequality, dimensionality), see Table I and [17] for details. The parameter values for each PSO variant were defined as follows: 80 particles and 2000 generations (160,000 evaluations), c 1 = 2.7 and c 2 = 2.5 for all PSO variants, for the two local best variants we used 8 neighborhoods, w = 0.7 for both inertia weight variants and k = 0.729 [14] for both constriction factor variants.…”

Section: A Experimental Designmentioning

confidence: 99%

Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization

Mendoza¹,

Mezura-Montes²

2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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Abstract-In this paper, we propose a novel approach to solve constrained optimization problems based on particle swarm optimization (PSO). First, an empirical comparison of the most popular PSO variants is presented as to select the most convenient among them. After that, the PSO variant chosen is improved in: (1) its parameter control with a dynamic proposal as to promote a better exploration of the search space and to avoid premature convergence and (2) its constraint-handling mechanism by using multiobjective concepts as to promote a better approach to the feasible region. The algorithm is tested on a set of 13 well-known benchmark problems and the obtained performance is compared against some PSO variants and stateof-the-art approaches. Based on the results presented some conclusions are drawn and the future work is established.

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A Simple Multimembered Evolution Strategy to Solve Constrained Optimization Problems

Cited by 531 publications

References 21 publications

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators

Dynamic adaptation and multiobjective concepts in a particle swarm optimizer for constrained optimization

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