Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems

Zahara, Erwie; Kao, Yu-Cheng

doi:10.1016/j.eswa.2008.02.039

Cited by 305 publications

(85 citation statements)

References 16 publications

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“…One of the common constrained engineering problems is the minimization of the weight of a tension/compression spring [1,2,[4][5][6][7]. The spring to be designed for minimum weight subject to constraints on minimum deflection, shear stress, surge frequency, limits on the outside diameter, and on design variables.…”

Section: Experimental Resuls and Discussionmentioning

confidence: 99%

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A Dynamic Quality-Based Harmony Search Algorithm for Solving Constrained Engineering Optimization Problems

Kattan¹

2013

Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering

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Abstract-Meta-heuristic algorithms overcome the computational drawbacks of the existing numerical methods where they are commonly used to solve constrained engineering problems and find an optimum solution. Solving this kind of problems is considered of high value in many engineering and manufacturing processes. The author have recently published a dynamic and self-adaptive meta-heuristic that is based on the harmony search algorithm. The method has the advantage of dynamically setting optimization parameters based on quality measures that are computed during the optimization process. Testing showed superiority in solving continuous optimization problems with high dimensionality. In this work, the same method is applied with minor modifications to solve constrained engineering problems whereby the feasible search-space is shrunk due to the existing constraints making the problem more difficult. The results obtained are close to those achieved by some other recent meta-heuristic methods. However, there is a room for improvement.

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Section: Experimental Resuls and Discussionmentioning

confidence: 99%

“…These meta-heuristics deploy stochastic techniques such as differential evolution (DE) [1,4], particle swarm optimization (PSO) [5,6] and harmony search (HS) [2,7]. Of particular interest is the recent HS approach.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Quality-Based Harmony Search Algorithm for Solving Constrained Engineering Optimization Problems

Kattan¹

2013

Proceedings of the 2nd International Conference on Advances in Computer Science and Engineering

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“…We also show the run-time behavior of ACO MV and CES MV by using RLDs. It is important to note that NM-PSO [Zahara and Kao, 2009] and PSOLVER [Kayhan et al, 2010] report infeasible solutions that violate the problems' constraints;Crepinsek et al [Crepinsek et al, 2012] pointed out that the authors of TLBO [Rao et al, 2011] used an incorrect formula for computing the number of objective function evaluations. Therefore, we did not include these three algorithms in our comparison.…”

Section: Application To Engineering Optimization Problemsmentioning

confidence: 99%

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

Liao¹

2015

4OR-Q J Oper Res

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ii SummaryContinuous optimization problems are optimization problems where all variables have a domain that typically is a subset of the real numbers; mixed discretecontinuous optimization problems have additionally other types of variables, so that some variables are continuous and others are on an ordinal or categorical scale. Continuous and mixed discrete-continuous problems have a wide range of applications in disciplines such as computer science, mechanical or electrical engineering, economics and bioinformatics. These problems are also often hard to solve due to their inherent difficulties such as a large number of variables, many local optima or other factors making problems hard. Therefore, in this thesis our focus is on the design, engineering and configuration of high-performing heuristic optimization algorithms.We tackle continuous and mixed discrete-continuous optimization problems with two classes of population-based heuristic algorithms, ant colony optimization (ACO) algorithms and evolution strategies. In a nutshell, the main contributions of this thesis are that (i) we advance the design and engineering of ACO algorithms to algorithms that are competitive or superior to recent state-of-the-art algorithms for continuous and mixed discrete-continuous optimization problems, (ii) we improve upon a specific state-of-the-art evolution strategy, the covariance matrix adaptation evolution strategy (CMA-ES), and (iii) we extend CMA-ES to tackle mixed discrete-continuous optimization problems.More in detail, we propose a unified ant colony optimization (ACO) framework for continuous optimization (UACOR). This framework synthesizes algorithmic components of two ACO algorithms that have been proposed in the literature and an incremental ACO algorithm with local search for continuous optimization, which we have proposed during my doctoral research. The design of UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new, high-performing ACO algorithms for continuous optimization. We also propose iCMAES-ILS, a hybrid algorithm that loosely couples IPOP-CMA-ES, a CMA-ES variant that uses a restart schema coupled with an increasing population iii size, and a new iterated local search (ILS) algorithm for continuous optimization. The hybrid algorithm consists of an initial competition phase, in which IPOP-CMA-ES and the ILS algorithm compete for further deployment during a second phase. A cooperative aspect of the hybrid algorithm is implemented in the form of some limited information exchange from IPOP-CMA-ES to the ILS algorithm during the initial phase. Experimental studies on recent benchmark functions suites show that UACOR and iCMAES-ILS are competitive or superior to other state-of-the-art algorithms.To tackle mixed discrete-continuous optimization problems, we extend ACO MV and propose CES MV , an ant colony optimization algorithm and a covariance matrix adaptation evolution strategy, respectively. In ACO MV and CES MV , the decision variables of an optimization prob...

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“…As it can be seen from this table, SA-MFO obtained better results compared with the standard MFO. Table 8 compares the optimal solution obtained for pressure vessel problem by SA-MFO with hybrid particle swarm optimization (HPSO) [10], co-evolutionary particle swarm optimization using Gaussian distribution (CPSOGD) [27], GA based on using of dominance-based tour tournament selection (GA-TTS) [3], co-evolutionary differential evolution (CDE) [12], PSO [15], hybrid Nelder-mead simplex search and particle swarm optimization (NMPSO) [41], Gaussian quantum-behaved particle swarm optimization (GQPSO) [1], and MFO. As it can be observed, SA-PSO and ACO obtained the optimal solutions compared with the other algorithms.…”

Section: Comparison Using Engineering Design Problems Experimentsmentioning

confidence: 99%

A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed

Hassanien

2018

Complex Intell. Syst.

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This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

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Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems

Cited by 305 publications

References 16 publications

A Dynamic Quality-Based Harmony Search Algorithm for Solving Constrained Engineering Optimization Problems

A Dynamic Quality-Based Harmony Search Algorithm for Solving Constrained Engineering Optimization Problems

Population-based heuristic algorithms for continuous and mixed discrete-continuous optimization problems

A hybrid SA-MFO algorithm for function optimization and engineering design problems

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