An incremental ant colony algorithm with local search for continuous optimization

Liao, Tianjun; de, Marco A. Montes; Aydın, Doğan; Stützle, Thomas; Dorigo, Marco

doi:10.1145/2001576.2001594

Cited by 59 publications

(46 citation statements)

References 24 publications

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“…We use irace ] to automatically configure two new ACO algorithms. Their competitive performance to other state-of-the-art algorithms shows the high potential ACO algorithms have for continuous optimization and the high potential automatic algorithm configuration techniques have for the development of continuous optimizers from 1 IACO R -LS [Liao et al, 2011b] is a significant algorithm component that contributes to UACOR. It is a variant of ACO R that uses local search and that features a growing solution archive.…”

Section: A Unified Ant Colony Optimization Framework For Continuous Omentioning

confidence: 99%

“…DACO R [Leguizamón and Coello, 2010] is another recent ACO algorithm for continuous optimization. IACO R -LS algorithm [Liao et al, 2011b] is a more recent variant that I have proposed during my doctoral research.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

“…The main details of the ACO R algorithm have already been described in Section 2.3.1 and we refer the reader to the details described there. Next we describe DACO R [Leguizamón and Coello, 2010] and IACO R -LS [Liao et al, 2011b], two more recent ACO algorithms for continuous optimization.…”

Section: Aco Algorithms For Continuous Optimizationmentioning

confidence: 99%

“…Leguizamón and Coello [2010] proposed an extension of ACO R , called DACO R , that had the goal of better maintaining diversity during the search. Subsequently, we proposed IACO R -LS, an incremental ant colony algorithm with local search for continuous optimization [Liao et al, 2011b]. IACO R -LS uses a growing solution archive as an extra search diversification mechanism and a local search to intensify the search.…”

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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: A Unified Ant Colony Optimization Framework For Continuous Omentioning

confidence: 99%

Section: Ant Colony Optimizationmentioning

confidence: 99%

Section: Aco Algorithms For Continuous Optimizationmentioning

confidence: 99%

mentioning

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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“…Moreover, irace allows to specify conditional parameters and therefore tune the parameters regarding a specific algorithm schemata, only when the specific algorithm schemata has been selected by a categorical parameter. These characteristics and a number of successful applications [5,6,13,14,15], made it the natural choice for tuning the parameters of our SLS algorithm.…”

Section: Off-line Tuning Of the Algorithmic Schematamentioning

confidence: 99%

A Non-adaptive Stochastic Local Search Algorithm for the CHeSC 2011 Competition

Mascia

Stützle

2012

Lecture Notes in Computer Science

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Abstract. In this work, we present our submission for the Cross-domain Heuristic Search Challenge 2011. We implemented a stochastic local search algorithm that consists of several algorithm schemata that have been offline-tuned on four sample problem domains. The schemata are based on all families of low-level heuristics available in the framework used in the competition with the exception of crossover heuristics. Our algorithm goes through an initial phase that filters dominated low-level heuristics, followed by an algorithm schemata selection implemented in a race. The winning schemata is run for the remaining computation time. Our algorithm ranked seventh in the competition results. In this paper, we present the results obtained after a more careful tuning, and a different combination of algorithm schemata included in the final algorithm design. This improved version would rank fourth in the competition.

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On the Reynolds Equation and the Load Problem in Lubrication: Literature Review and Mathematical Modelling

Rodríguez

Tello²

2019

Studies in Systems, Decision and Control

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An incremental ant colony algorithm with local search for continuous optimization

Cited by 59 publications

References 24 publications

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

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

A Non-adaptive Stochastic Local Search Algorithm for the CHeSC 2011 Competition

On the Reynolds Equation and the Load Problem in Lubrication: Literature Review and Mathematical Modelling

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