A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test

LaTorre, Antonio; Muelas, Santiago; Peña, José-María

doi:10.1007/s00500-010-0646-3

Cited by 98 publications

(52 citation statements)

References 22 publications

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“…UACOR-s performs statistically significantly better than these two algorithms. The best performing algorithm from the SOCO competition is MOS-DE [LaTorre et al, 2011], an algorithm that combines differential evolution and the Mtsls1 local search algorithm. It is noteworthy that UACOR-s performs competitive to MOS-DE.…”

Section: Experiments On Soco Benchmark Setmentioning

confidence: 99%

“…For this chapter, we present a new iterated local search algorithm (labeled as ILS), where a high-performing derivative-free local search algorithm for continuous optimization, Mtsls1 [Tseng and Chen, 2008], is used. The Mtsls1 local search was chosen since it is a crucial component of several highperforming algorithms for continuous optimization [LaTorre et al, 2011, Liao et al, 2011b. Mtsls1 iteratively searches along dimensions one by one in a certain step size.…”

Section: Ilsmentioning

confidence: 99%

“…It is also known that many high performing state-of-the-art algorithms for continuous optimization rely on the usage of effective local search algorithms. An example is the MOS-DE algorithm, the algorithm that was identified in the special issue as the best performing one on the SOCO benchmark functions [LaTorre et al, 2011]. Other examples include memetic algortihms [Molina et al, 2010a] or hybrid ant colony optimization algorithms [Liao et al, 2011b].…”

Section: E32 Local Searchmentioning

confidence: 99%

“…We have computed for all the algorithms the distribution of the median error values found from the publicly available results tables and the boxplots in Figure E.12 compare these distributions across several dimensions. In fact, GbABC reaches a median performance that is competitive to the best continuous optimizers from the competition; for example, it reaches for 50 and 100 dimensions a median error that is below the optimum threshold for 14 functions, the same as the overall best performing algorithm in the benchmark competition, MOS-DE [LaTorre et al, 2011]. A further statistical analysis (using Wilcoxon's test with Holm's correction for multiple comparisons) shows that GbABC is statistically significantly better than reference algorithms DE, CHC and G-CMA-ES as well as some contributors such as SOUPDE, GODE, MA-SSW, RPSO-vm, EvoPROpt and EM323 for dimensions 100 and 500.…”

Section: E43 Comparison With Soco Special Issue Contributorsmentioning

confidence: 99%

“…Third, we consider the integration of local search algorithms into the ABC variants. In fact, many recent state-ofthe-art algorithms for continuous optimization combine an effective local search mechanism with population-based search techniques [LaTorre et al, 2011, Molina et al, 2010a and, thus, it is natural to consider this possibility also here. In this final step, we again use the help of automatic algorithm configuration tools to avoid a manuel re-design of the hybrid algorithms and to ensure an even tuning of the various ABC hybrid algorithms.…”

Section: E1 Introductionmentioning

confidence: 99%

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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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Section: Experiments On Soco Benchmark Setmentioning

confidence: 99%

Section: Ilsmentioning

confidence: 99%

Section: E32 Local Searchmentioning

confidence: 99%

Section: E43 Comparison With Soco Special Issue Contributorsmentioning

confidence: 99%

Section: E1 Introductionmentioning

confidence: 99%

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

Liao¹

2015

4OR-Q J Oper Res

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SoftFEM: The Soft Finite Element Method

Peña

LaTorre

Jérusalem

2019

Numerical Meth Engineering

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Summary While the finite element method (FEM) has now reached full maturity both in academy and industry, its use in optimization pipelines remains either computationally intensive or cumbersome. In particular, currently used optimization schemes leveraging FEM still require the choice of dedicated optimization algorithms for a specific design problem, and a “black box” approach to FEM‐based optimization remains elusive. To this end, we propose here an integrated finite element‐soft computing method, ie, the soft FEM (SoftFEM), which integrates a finite element solver within a metaheuristic search wrapper. To illustrate this general method, we focus here on solid mechanics problems. For these problems, SoftFEM is able to optimize geometry changes and mechanistic measures based on geometry constraints and material properties inputs. From the optimization perspective, the use of a fitness function based on finite element calculation imposes a series of challenges. To bypass the limitations in search capabilities of the usual optimization techniques (local search and gradient‐based methods), we propose, instead a hybrid self adaptive search technique, the multiple offspring sampling (MOS), combining two metaheuristics methods: one population‐based differential evolution method and a local search optimizer. The formulation coupling FEM to the optimization wrapper is presented in detail and its flexibility is illustrated with three representative solid mechanics problems. More particularly, we propose here the MOS as the most versatile search algorithm for SoftFEM. A new method for the identification of nonfully determined parameters is also proposed.

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