Water Distribution System Computer‐Aided Design by Agent Swarm Optimization

Montalvo, Idel; Izquierdo, Joaquín; Pérez-García, Rafael; Herrera, Manuel

doi:10.1111/mice.12062

Cited by 59 publications

(50 citation statements)

References 68 publications

(76 reference statements)

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“…Recently optimization problems have recently been studied in both industrial and scientific contexts [9,15,43,52,72]. It is essential to select the appropriate optimization algorithm for practical engineering purposes.…”

Section: The Estimation-of-distribution Algorithm For Optimization Prmentioning

confidence: 99%

Quantitative optimization of interoperability during feature-based data exchange

Zhang

Han

et al. 2015

ICA

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Abstract. Sharing feature-based computer-aided design (CAD) models is a challenging problem that is frequently encountered among heterogeneous CAD systems. In this work, a new asymmetric strategy is presented to enrich the theory of feature-based interoperability, particularly when addressing a singular feature or singular sketch. This paper analyzes the semantic asymmetry singular feature interoperability (SA-SFI) and parameter asymmetry singular sketch interoperability (PA-SSI) in detail. We pay special attention to the problem of PA-SSI, which is universally significant in collaborative product development (CPD). The objective of PA-SSI is to develop an optimized model to exchange a singular sketch (spline) to ensure that the exchanged model both maintains high geometric fidelity and can be effectively edited in the target CAD system. The proposed method applies the estimation of distribution algorithm (EDA) to automatically solve this problem, and a Gaussian mixture model (GMM) is built according to the promising solutions. Furthermore, Hausdorff distance is adopted to calculate the fitness, and a local optimization operator is designed to enhance the global search capability of the population. Experimental results demonstrate that the proposed approach can maintain a sufficiently high geometric fidelity, and ensure that the exchanged model of the target CAD system can be parametrically edited.

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Section: The Estimation-of-distribution Algorithm For Optimization Prmentioning

confidence: 99%

Quantitative optimization of interoperability during feature-based data exchange

Zhang

Han

et al. 2015

ICA

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“…The flexibility introduced by evolutionary algorithms has enabled the use of virtually any objective function for evaluating solutions, even when these evaluations require running complex mathematical and/or procedural simulations of the systems under analysis. The literature is very extensive in examples, in particular, in urban hydraulics (Liong and Atiquzzama, 2004;Geem, 2006;Izquierdo et al, 2008b;Jin et al, 2008;Montalvo et al, 2010;Shean and McBean, 2010;Bei and Dandy, 2012;Berardi, Laucelli, and Giustolisi, 2012;Wu and Behandish, 2012;Montalvo et al, 2014;Marchi et al, 2014). See also (Zheng, Simpson, and Zecchin, 2013a) for a review.…”

Section: Introductionmentioning

confidence: 99%

A hybrid, auto-adaptive and rule-based multi-agent approach using evolutionary algorithms for improved searching

Izquierdo

Montalvo²,

Campbell

et al. 2015

Engineering Optimization

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Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. (2015). A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching. Engineering Optimization. 1-13. doi:10.1080/0305215X.2015.1107434.A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching Selecting the most appropriate heuristic for solving a specific problem is not easy. The reasons are manifold. This article focus on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same no matter the size, complexity, and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm through the use of techniques derived from knowledge discovery (data mining techniques) on databases of so-far visited solutions. The aim is to improve search mechanisms, increase computational efficiency, and enrich through rules the formulation of optimization problems -while reducing the search space and catering to realistic problems.

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“…The initialization and evolution of the individuals involve random influence; therefore, the set of nondominated solutions obtained by a metaheuristic might be quite different for each run. Agent swarm optimization [11] improves the robustness by using a framework with various population-based metaheuristics coexist, working like a multi-agent system. A number of adaptive strategies have been adopted recently in literature metaheuristics to enhance the search efficiency [17,18,26,27].…”

Section: Related Workmentioning

confidence: 99%

“…In each iteration, each individual is compared with its evolved value in order to decide whether the individual needs to be replaced by its evolved value. For metaheuristics that treat the multiobjective optimization problem as whole [6][7][8][9][10][11][12][13][14][15][16], the comparison is based on Pareto dominance. As Pareto dominance is a partial order, an inappropriate selection of the value to stay in the population would negatively affect discovering the true Pareto front.…”

Section: Related Workmentioning

confidence: 99%

“…Compared with traditional optimization methods such as linear programming, nonlinear programming, and optimal control theory, metaheuristics do not require characteristics such as continuity, differentiability, linearity, and convexity on the optimization problem; in addition, population-based metaheuristics can find multiple nondominated solutions in just one run for multiobjective optimization. Existing metaheuristics solve a multiobjective optimization problem by either treating the multiobjective problem as whole [6][7][8][9][10][11][12][13][14][15][16][17][18] or involving decomposition (i.e., decomposing the multiobjective problem into multiple single-objective problems) [1,[19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

Estevez

2018

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Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found so far. MSCLPSO can approximate the true Pareto front in one single run; however, it requires a large number of generations to converge, because each swarm only optimizes the associated objective and does not learn from any search experience outside the swarm. In this paper, we propose an adaptive particle velocity update strategy for MSCLPSO to improve the search efficiency. Based on whether the elitists are indifferent or complex on each dimension, each particle adaptively determines whether to just learn from some particle in the same swarm, or additionally from the difference of some pair of elitists for the velocity update on that dimension, trying to achieve a tradeoff between optimizing the associated objective and exploring diverse regions of the Pareto set. Experimental results on various two-objective and three-objective benchmark optimization problems with different dimensional complexity characteristics demonstrate that the adaptive particle velocity update strategy improves the search performance of MSCLPSO significantly and is able to help MSCLPSO locate the true Pareto front more quickly and obtain better distributed nondominated solutions over the entire Pareto front.

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Water Distribution System Computer‐Aided Design by Agent Swarm Optimization

Abstract: Optimal design of water distribution systems (WDS), including the sizing of components

Cited by 59 publications

References 68 publications

Quantitative optimization of interoperability during feature-based data exchange

Quantitative optimization of interoperability during feature-based data exchange

A hybrid, auto-adaptive and rule-based multi-agent approach using evolutionary algorithms for improved searching

Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

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