The Experts below are selected from a list of 97218 Experts worldwide ranked by ideXlab platform
Marco Dorigo - One of the best experts on this subject based on the ideXlab platform.
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a unified ant colony Optimization algorithm for Continuous Optimization
European Journal of Operational Research, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco DorigoAbstract:In this article, we propose UACOR, a unified ant colony Optimization (ACO) algorithm for Continuous Optimization. UACOR includes algorithmic components from ACOR,DACOR and IACOR-LS, three ACO algorithms for Continuous Optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms; in addition, from UACOR we can also generate new Continuous ACO algorithms that have not been considered before in the literature. In fact, UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new ACO algorithms. To show the benefits of UACOR’s flexibility, we automatically configure two new ACO algorithms, UACOR-s and UACOR-c, and evaluate them on two sets of benchmark functions from a recent special issue of the Soft Computing (SOCO) journal and the IEEE 2005 Congress on Evolutionary Computation (CEC’05), respectively. We show that UACOR-s is competitive with the best of the 19 algorithms benchmarked on the SOCO benchmark set and that UACOR-c performs superior to IPOP-CMA-ES and statistically significantly better than five other algorithms benchmarked on the CEC’05 set. These results show the high potential ACO algorithms have for Continuous Optimization and suggest that automatic algorithm configuration is a viable approach for designing state-of-the-art Continuous optimizers.
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Ant Colony Optimization for Mixed-Variable Optimization Problems
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco Montes A De Oca, Krzysztof Socha, Marco DorigoAbstract:In this paper, we introduce ACOMV: an ant colony Optimization (ACO) algorithm that extends the ACOR algorithm for Continuous Optimization to tackle mixed-variable Optimization problems. In ACOMV, the decision variables of an Optimization problem can be explicitly declared as Continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACOMV includes three solution generation mechanisms: a Continuous Optimization mechanism (ACOR), a Continuous relaxation mechanism (ACOMV-o) for ordinal variables, and a categorical Optimization mechanism (ACOMV-c) for categorical variables. Together, these mechanisms allow ACOMV to tackle mixed-variable Optimization problems. We also define a novel procedure to generate artificial, mixed-variable benchmark functions, and we use it to automatically tune ACOMV's parameters. The tuned ACOMV is tested on various real-world Continuous and mixed-variable engineering Optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACOMV on mixed-variable Optimization problems.
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an incremental ant colony algorithm with local search for Continuous Optimization
Genetic and Evolutionary Computation Conference, 2011Co-Authors: Tianjun Liao, Thomas Stutzle, Dogan Aydin, Marco DorigoAbstract:ACOR is one of the most popular ant colony Optimization algorithms for tackling Continuous Optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on Continuous Optimization problems.
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ant colony Optimization for Continuous domains
European Journal of Operational Research, 2008Co-Authors: Krzysztof Socha, Marco DorigoAbstract:In this paper we present an extension of ant colony Optimization (ACO) to Continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial Optimization, can be adapted to Continuous Optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other Continuous Optimization methods: other ant-related approaches and other metaheuristics initially developed for combinatorial Optimization and later adapted to handle the Continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
Mustafa Servet Kiran - One of the best experts on this subject based on the ideXlab platform.
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tsa tree seed algorithm for Continuous Optimization
Expert Systems With Applications, 2015Co-Authors: Mustafa Servet KiranAbstract:Abstract This paper presents a new intelligent optimizer based on the relation between trees and their seeds for Continuous Optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n-dimensional search space corresponds with the possible solution of an Optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm Optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function Optimization and is an alternative Optimization method for solving multilevel thresholding problem.
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artificial bee colony algorithm with variable search strategy for Continuous Optimization
Information Sciences, 2015Co-Authors: Mustafa Servet Kiran, Huseyin Hakli, Mesut Gunduz, Harun UguzAbstract:The artificial bee colony (ABC) algorithm is a swarm-based Optimization technique proposed for solving Continuous Optimization problems. The artificial agents of the ABC algorithm use one solution update rule during the search process. To efficiently solve Optimization problems with different characteristics, we propose the integration of multiple solution update rules with ABC in this study. The proposed method uses five search strategies and counters to update the solutions. During initialization, each update rule has a constant counter content. During the search process performed by the artificial agents, these counters are used to determine the rule that is selected by the bees. Because the Optimization problems and functions have different characteristics, one or more search strategies are selected and are used during the iterations according to the characteristics of the numeric functions in the proposed approach. By using the search strategies and mechanisms proposed in the present study, the artificial agents learn which update rule is more appropriate based on the characteristics of the problem to find better solutions. The performance and accuracy of the proposed method are examined on 28 numerical benchmark functions, and the obtained results are compared with various classical versions of ABC and other nature-inspired Optimization algorithms. The experimental results show that the proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments.
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a recombination based hybridization of particle swarm Optimization and artificial bee colony algorithm for Continuous Optimization problems
Soft Computing, 2013Co-Authors: Mustafa Servet Kiran, Mesut GunduzAbstract:This paper presents a hybridization of particle swarm Optimization (PSO) and artificial bee colony (ABC) approaches, based on recombination procedure. The PSO and ABC are population-based iterative methods. While the PSO directly uses the global best solution of the population to determine new positions for the particles at the each iteration, agents (employed, onlooker and scout bees) of the ABC do not directly use this information but the global best solution in the ABC is stored at the each iteration. The global best solutions obtained by the PSO and ABC are used for recombination, and the solution obtained from this recombination is given to the populations of the PSO and ABC as the global best and neighbor food source for onlooker bees, respectively. Information flow between particle swarm and bee colony helps increase global and local search abilities of the hybrid approach which is referred to as Hybrid approach based on Particle swarm Optimization and Artificial bee colony algorithm, HPA for short. In order to test the performance of the HPA algorithm, this study utilizes twelve basic numerical benchmark functions in addition to CEC2005 composite functions and an energy demand estimation problem. The experimental results obtained by the HPA are compared with those of the PSO and ABC. The performance of the HPA is also compared with that of other hybrid methods based on the PSO and ABC. The experimental results show that the HPA algorithm is an alternative and competitive optimizer for Continuous Optimization problems.
Thomas Stutzle - One of the best experts on this subject based on the ideXlab platform.
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a unified ant colony Optimization algorithm for Continuous Optimization
European Journal of Operational Research, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco DorigoAbstract:In this article, we propose UACOR, a unified ant colony Optimization (ACO) algorithm for Continuous Optimization. UACOR includes algorithmic components from ACOR,DACOR and IACOR-LS, three ACO algorithms for Continuous Optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms; in addition, from UACOR we can also generate new Continuous ACO algorithms that have not been considered before in the literature. In fact, UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new ACO algorithms. To show the benefits of UACOR’s flexibility, we automatically configure two new ACO algorithms, UACOR-s and UACOR-c, and evaluate them on two sets of benchmark functions from a recent special issue of the Soft Computing (SOCO) journal and the IEEE 2005 Congress on Evolutionary Computation (CEC’05), respectively. We show that UACOR-s is competitive with the best of the 19 algorithms benchmarked on the SOCO benchmark set and that UACOR-c performs superior to IPOP-CMA-ES and statistically significantly better than five other algorithms benchmarked on the CEC’05 set. These results show the high potential ACO algorithms have for Continuous Optimization and suggest that automatic algorithm configuration is a viable approach for designing state-of-the-art Continuous optimizers.
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Ant Colony Optimization for Mixed-Variable Optimization Problems
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco Montes A De Oca, Krzysztof Socha, Marco DorigoAbstract:In this paper, we introduce ACOMV: an ant colony Optimization (ACO) algorithm that extends the ACOR algorithm for Continuous Optimization to tackle mixed-variable Optimization problems. In ACOMV, the decision variables of an Optimization problem can be explicitly declared as Continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACOMV includes three solution generation mechanisms: a Continuous Optimization mechanism (ACOR), a Continuous relaxation mechanism (ACOMV-o) for ordinal variables, and a categorical Optimization mechanism (ACOMV-c) for categorical variables. Together, these mechanisms allow ACOMV to tackle mixed-variable Optimization problems. We also define a novel procedure to generate artificial, mixed-variable benchmark functions, and we use it to automatically tune ACOMV's parameters. The tuned ACOMV is tested on various real-world Continuous and mixed-variable engineering Optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACOMV on mixed-variable Optimization problems.
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benchmark results for a simple hybrid algorithm on the cec 2013 benchmark set for real parameter Optimization
Congress on Evolutionary Computation, 2013Co-Authors: Tianjun Liao, Thomas StutzleAbstract:In this article, we benchmark a new hybrid algorithm for Continuous Optimization on the 28 functions for the CEC 2013 special session and competition on real-parameter Optimization. Our algorithm makes a loose coupling of (i) IPOP-CMA-ES, an advanced evolution strategy with covariance matrix adaptation integrated with an occasional restart strategy and increasing population size, and (ii) an iterated local search (ILS) algorithm that repeatedly applies a different local search from CMA-ES to perturbations of previous high-quality solutions. The central idea of the hybrid algorithm is to let IPOP-CMA-ES and ILS compete in an initial competition phase and then the winner of the two algorithms is deployed for the remainder of the computation time. A cooperative element between the two algorithms is implemented through a solution exchange from IPOP-CMA-ES to ILS. Hence, one may classify this algorithm as a loosely coupled cooperative-competitive algorithm for Continuous Optimization. We compare the computational results of this hybrid algorithm to the default version and a tuned version of IPOP-CMA-ES to illustrate the improvement that is obtained through this hybrid algorithm. This comparison is interesting since IPOP-CMA-ES is a state-of-the-art algorithm which somehow has become a standard benchmark to compare against for any new algorithmic proposals for Continuous Optimization. Our computational results show that the proposed hybrid algorithm performs significantly better than the default and tuned IPOP-CMA-ES variants on the problems of dimension 30 and 50. Thus, these results also indicate that the hybrid algorithm reaches very high performance on the CEC 2013 benchmark set.
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Continuous Optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms
Swarm Intelligence, 2012Co-Authors: Zhi Yuan, Marco Montes A De Oca, Mauro Birattari, Thomas StutzleAbstract:The performance of Optimization algorithms, including those based on swarm intelligence, depends on the values assigned to their parameters. To obtain high performance, these parameters must be fine-tuned. Since many parameters can take real values or integer values from a large domain, it is often possible to treat the tuning problem as a Continuous Optimization problem. In this article, we study the performance of a number of prominent Continuous Optimization algorithms for parameter tuning using various case studies from the swarm intelligence literature. The Continuous Optimization algorithms that we study are enhanced to handle the stochastic nature of the tuning problem. In particular, we introduce a new post-selection mechanism that uses F-Race in the final phase of the tuning process to select the best among elite parameter configurations. We also examine the parameter space of the swarm intelligence algorithms that we consider in our study, and we show that by fine-tuning their parameters one can obtain substantial improvements over default configurations.
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an incremental ant colony algorithm with local search for Continuous Optimization
Genetic and Evolutionary Computation Conference, 2011Co-Authors: Tianjun Liao, Thomas Stutzle, Dogan Aydin, Marco DorigoAbstract:ACOR is one of the most popular ant colony Optimization algorithms for tackling Continuous Optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on Continuous Optimization problems.
Tianjun Liao - One of the best experts on this subject based on the ideXlab platform.
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a unified ant colony Optimization algorithm for Continuous Optimization
European Journal of Operational Research, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco DorigoAbstract:In this article, we propose UACOR, a unified ant colony Optimization (ACO) algorithm for Continuous Optimization. UACOR includes algorithmic components from ACOR,DACOR and IACOR-LS, three ACO algorithms for Continuous Optimization that have been proposed previously. Thus, it can be used to instantiate each of these three earlier algorithms; in addition, from UACOR we can also generate new Continuous ACO algorithms that have not been considered before in the literature. In fact, UACOR allows the usage of automatic algorithm configuration techniques to automatically derive new ACO algorithms. To show the benefits of UACOR’s flexibility, we automatically configure two new ACO algorithms, UACOR-s and UACOR-c, and evaluate them on two sets of benchmark functions from a recent special issue of the Soft Computing (SOCO) journal and the IEEE 2005 Congress on Evolutionary Computation (CEC’05), respectively. We show that UACOR-s is competitive with the best of the 19 algorithms benchmarked on the SOCO benchmark set and that UACOR-c performs superior to IPOP-CMA-ES and statistically significantly better than five other algorithms benchmarked on the CEC’05 set. These results show the high potential ACO algorithms have for Continuous Optimization and suggest that automatic algorithm configuration is a viable approach for designing state-of-the-art Continuous optimizers.
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Ant Colony Optimization for Mixed-Variable Optimization Problems
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco Montes A De Oca, Krzysztof Socha, Marco DorigoAbstract:In this paper, we introduce ACOMV: an ant colony Optimization (ACO) algorithm that extends the ACOR algorithm for Continuous Optimization to tackle mixed-variable Optimization problems. In ACOMV, the decision variables of an Optimization problem can be explicitly declared as Continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACOMV includes three solution generation mechanisms: a Continuous Optimization mechanism (ACOR), a Continuous relaxation mechanism (ACOMV-o) for ordinal variables, and a categorical Optimization mechanism (ACOMV-c) for categorical variables. Together, these mechanisms allow ACOMV to tackle mixed-variable Optimization problems. We also define a novel procedure to generate artificial, mixed-variable benchmark functions, and we use it to automatically tune ACOMV's parameters. The tuned ACOMV is tested on various real-world Continuous and mixed-variable engineering Optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACOMV on mixed-variable Optimization problems.
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benchmark results for a simple hybrid algorithm on the cec 2013 benchmark set for real parameter Optimization
Congress on Evolutionary Computation, 2013Co-Authors: Tianjun Liao, Thomas StutzleAbstract:In this article, we benchmark a new hybrid algorithm for Continuous Optimization on the 28 functions for the CEC 2013 special session and competition on real-parameter Optimization. Our algorithm makes a loose coupling of (i) IPOP-CMA-ES, an advanced evolution strategy with covariance matrix adaptation integrated with an occasional restart strategy and increasing population size, and (ii) an iterated local search (ILS) algorithm that repeatedly applies a different local search from CMA-ES to perturbations of previous high-quality solutions. The central idea of the hybrid algorithm is to let IPOP-CMA-ES and ILS compete in an initial competition phase and then the winner of the two algorithms is deployed for the remainder of the computation time. A cooperative element between the two algorithms is implemented through a solution exchange from IPOP-CMA-ES to ILS. Hence, one may classify this algorithm as a loosely coupled cooperative-competitive algorithm for Continuous Optimization. We compare the computational results of this hybrid algorithm to the default version and a tuned version of IPOP-CMA-ES to illustrate the improvement that is obtained through this hybrid algorithm. This comparison is interesting since IPOP-CMA-ES is a state-of-the-art algorithm which somehow has become a standard benchmark to compare against for any new algorithmic proposals for Continuous Optimization. Our computational results show that the proposed hybrid algorithm performs significantly better than the default and tuned IPOP-CMA-ES variants on the problems of dimension 30 and 50. Thus, these results also indicate that the hybrid algorithm reaches very high performance on the CEC 2013 benchmark set.
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an incremental ant colony algorithm with local search for Continuous Optimization
Genetic and Evolutionary Computation Conference, 2011Co-Authors: Tianjun Liao, Thomas Stutzle, Dogan Aydin, Marco DorigoAbstract:ACOR is one of the most popular ant colony Optimization algorithms for tackling Continuous Optimization problems. In this paper, we propose IACOR-LS, which is a variant of ACOR that uses local search and that features a growing solution archive. We experiment with Powell's conjugate directions set, Powell's BOBYQA, and Lin-Yu Tseng's Mtsls1 methods as local search procedures. Automatic parameter tuning results show that IACOR-LS with Mtsls1 (IACOR-Mtsls1) is not only a significant improvement over ACOR, but that it is also competitive with the state-of-the-art algorithms described in a recent special issue of the Soft Computing journal. Further experimentation with IACOR-Mtsls1 on an extended benchmark functions suite, which includes functions from both the special issue of Soft Computing and the IEEE 2005 Congress on Evolutionary Computation, demonstrates its good performance on Continuous Optimization problems.
Krzysztof Socha - One of the best experts on this subject based on the ideXlab platform.
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Ant Colony Optimization for Mixed-Variable Optimization Problems
IEEE Transactions on Evolutionary Computation, 2014Co-Authors: Tianjun Liao, Thomas Stutzle, Marco Montes A De Oca, Krzysztof Socha, Marco DorigoAbstract:In this paper, we introduce ACOMV: an ant colony Optimization (ACO) algorithm that extends the ACOR algorithm for Continuous Optimization to tackle mixed-variable Optimization problems. In ACOMV, the decision variables of an Optimization problem can be explicitly declared as Continuous, ordinal, or categorical, which allows the algorithm to treat them adequately. ACOMV includes three solution generation mechanisms: a Continuous Optimization mechanism (ACOR), a Continuous relaxation mechanism (ACOMV-o) for ordinal variables, and a categorical Optimization mechanism (ACOMV-c) for categorical variables. Together, these mechanisms allow ACOMV to tackle mixed-variable Optimization problems. We also define a novel procedure to generate artificial, mixed-variable benchmark functions, and we use it to automatically tune ACOMV's parameters. The tuned ACOMV is tested on various real-world Continuous and mixed-variable engineering Optimization problems. Comparisons with results from the literature demonstrate the effectiveness and robustness of ACOMV on mixed-variable Optimization problems.
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ant colony Optimization for Continuous domains
European Journal of Operational Research, 2008Co-Authors: Krzysztof Socha, Marco DorigoAbstract:In this paper we present an extension of ant colony Optimization (ACO) to Continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial Optimization, can be adapted to Continuous Optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other Continuous Optimization methods: other ant-related approaches and other metaheuristics initially developed for combinatorial Optimization and later adapted to handle the Continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
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An ant colony Optimization algorithm for Continuous Optimization: application to feed-forward neural network training
Neural Computing and Applications, 2007Co-Authors: Krzysztof Socha, Christian BlumAbstract:Ant colony Optimization (ACO) is an Optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete Optimization problems. Recently we proposed a first ACO variant for Continuous Optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.
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aco for Continuous and mixed variable Optimization
Ant Colony Optimization and Swarm Intelligence, 2004Co-Authors: Krzysztof SochaAbstract:This paper presents how the Ant Colony Optimization (ACO) metaheuristic can be extended to Continuous search domains and applied to both Continuous and mixed discrete-Continuous Optimization problems. The paper describes the general underlying idea, enumerates some possible design choices, presents a first implementation, and provides some preliminary results obtained on well-known benchmark problems. The proposed method is compared to other ant, as well as non-ant methods for Continuous Optimization.