Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

Kaveh, A.; Shahrouzi, Mohsen

doi:10.1002/nme.2088

Cited by 13 publications

(6 citation statements)

References 24 publications

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“…The adaptive method (b) was considered more effective in solving complex problems, as a feedback from the optimization process is set to adjust parameter values during optimization progresses [26,54]. The aforementioned technique was adopted by many researchers to improve algorithms' performance, as in [30,49,[55][56][57], however none of these (and others) develop a self-adaptive technique that is sensitive to optimality achievement during evaluation progress [31].…”

Section: Self-adaptive Mechanism and Formulaementioning

confidence: 99%

Assessment of Water Resources Management Strategy Under Different Evolutionary Optimization Techniques

Al-Jawad

Kalin

2019

Water

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Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the -DSEA’s results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.

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Section: Self-adaptive Mechanism and Formulaementioning

confidence: 99%

Assessment of Water Resources Management Strategy Under Different Evolutionary Optimization Techniques

Al-Jawad

Kalin

2019

Water

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“…Among these algorithms, GAs have been broadly applied to solve various structural optimization problems, Kaveh and Kalatjari [2][3][4], Kaveh and Abditehrani [5], Kaveh and Rahami [6,7], Adeli and Cheng [8], Rajeev and Krishnamoorthy [9,10], Koumousis and Georgious [11], Hajela and Lee [12], Adeli and Kumar [13], Wu and Chow [14,15], Soh and Yang [16], Camp et al [17], Shrestha and Ghaboussi [18], Erbatur et al [19], and Sarma and Adeli [20] among many others. Ant Colony Optimization (ACO) is a new approach for structural design inspired by natural phenomena applied to different optimization problems by Kaveh [21][22][23], and Camp and Bichon [24].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization for Design of Space Trusses

Kaveh

Azar

Talatahari

2008

International Journal of Space Structures

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This paper presents two mechanisms to improve Ant Colony Optimization (ACO) for design of truss structures. The Sub-Optimization Mechanism causes the reduction in the search space, the size of the trail matrix and the decision vector; consequently, the optimization process is performed in smaller time. The Scatter Memory Mechanism introduces a matrix for saving the best scatter order and enables the ACO-based algorithms to reduce the number of ants and intensity of the analyses. The proposed mechanisms applied to ACS and ASrank are tested on two benchmark trusses from literature. The results for different algorithms demonstrate the efficiency of the proposed mechanisms.

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“…HPSACO utilizes a particle swarm optimization with a passive congregation algorithm as a global search, and the idea of ant colony approach worked as a local search. However, it is known that the PSO algorithm had difficulties in controlling the balance between exploration (global investigation of the search place) and exploitation (the fine search around a local optimum) [36].Ant colony optimization (ACO) was first proposed by Dorigo [37, 38] as a multi-agent approach to solve difficult combinatorial optimization problems and it has been applied to various engineering problems in recent years [39][40][41][42][43][44]. In the discrete HPSACO, agents are allowed to select discrete values from the permissible list of cross sections.…”

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confidence: 99%

“…Ant colony optimization (ACO) was first proposed by Dorigo [37, 38] as a multi-agent approach to solve difficult combinatorial optimization problems and it has been applied to various engineering problems in recent years [39][40][41][42][43][44]. ACO Hybrid Algorithm of Harmony Search, Particle Swarm and Ant Colony 161 was inspired by the observation of real ant colonies.…”

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confidence: 99%

Hybrid Algorithm of Harmony Search, Particle Swarm and Ant Colony for Structural Design Optimization

Kaveh

Talatahari

2009

Studies in Computational Intelligence

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This chapter considers the implementation of the heuristic particle swarm ant colony optimization (HPSACO) methodology to find an optimum design of different types of structures. HPSACO is an efficient hybridized approach based on the harmony search scheme, particle swarm optimizer, and ant colony optimization. HPSACO utilizes a particle swarm optimization with a passive congregation algorithm as a global search, and the idea of ant colony approach worked as a local search. The harmony search-based mechanism is used to handle the variable constraints. In the discrete HPSACO, agents are allowed to select discrete values from the permissible list of cross sections. The efficiency of the HPSACO algorithm is investigated to find an optimum design of truss structures with continuous or discrete search domains and for frame structures with a discrete search domain. The results indicate that the HPSACO is a quite effective algorithm to find the optimum solution of structural optimization problems with continuous or discrete variables. 160A. Kaveh and S. Talatahari does not necessarily correspond to the global optimum or even the neighborhood of it, in some cases.The computational drawbacks of classical numerical methods have forced researchers to rely on heuristic algorithms such as genetic algorithms (GAs), particle swarm optimizer (PSO), ant colony optimization (ACO) and harmony search (HS). These methods have attracted a great deal of attention, because of their high potential for modeling engineering problems in environments which have been resistant to a solution by classic techniques. They do not require gradient information and possess better global search abilities than the conventional optimization algorithms. Although these are approximate methods (i.e. their solutions are good, but not provably optimal), they do not require the derivatives of the objective function and constraints [3]. Having in common the processes of natural evolution, these algorithms share many similarities: each maintains a population of solutions which are evolved through random alterations and selection. The differences between these procedures lie in the representation technique utilized to encode the candidates, the type of alterations used to create new solutions, and the mechanism employed for selecting new patterns.The genetic algorithm is one of the heuristic algorithms initially suggested by Holland, and developed and extended by some of his students, Goldberg and De Jong. These algorithms simulate a natural genetics mechanism for synthetic systems based on operators that are duplicates of natural ones. In the last decade, GA has been used in the optimum structural design. One of the first applications was the weight minimization of a 10-bar truss by Goldberg and Samtani [4]. Also, many researchers have used genetic search in the design of various structures in which the search space was non-convex or discrete, Hajela [5], Rajeev and Krishnamoorthy [6,7], Koumousis and Georgious [8], Hajela and Lee [9], Wu and Chow [10], Soh and...

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Dynamic selective pressure using hybrid evolutionary and ant system strategies for structural optimization

Cited by 13 publications

References 24 publications

Assessment of Water Resources Management Strategy Under Different Evolutionary Optimization Techniques

Assessment of Water Resources Management Strategy Under Different Evolutionary Optimization Techniques

Ant Colony Optimization for Design of Space Trusses

Hybrid Algorithm of Harmony Search, Particle Swarm and Ant Colony for Structural Design Optimization

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