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Ant colony optimization for continuous domains
, 2008
"... 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 presen ..."
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Cited by 72 (5 self)
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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.
Orthogonal methods based ant colony search for solving continuous optimization problems
- J. Comput. Sci. Technol
, 2008
"... Abstract Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide ran ..."
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Cited by 8 (1 self)
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Abstract Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed “continuous orthogonal ant colony ” (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an “adaptive regional radius ” method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization — API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.
Neurodynamical optimization
- J. Global Opt
, 1994
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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Cited by 7 (4 self)
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of
Continuous function optimization using hybrid ant colony approach with orthogonal design scheme
- Proceeding of SEAL 2006, LNCS 4247, 2006, Page(s): 126
"... Abstract. A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following princip ..."
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Cited by 3 (1 self)
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Abstract. A hybrid Orthogonal Scheme Ant Colony Optimization (OSACO) algorithm for continuous function optimization (CFO) is presented in this paper. The methodology integrates the advantages of Ant Colony Optimization (ACO) and Orthogonal Design Scheme (ODS). OSACO is based on the following principles: a) each independent variable space (IVS) of CFO is dispersed into a number of random and movable nodes; b) the carriers of pheromone of ACO are shifted to the nodes; c) solution path can be obtained by choosing one appropriate node from each IVS by ant; d) with the ODS, the best solved path is further improved. The proposed algorithm has been successfully applied to 10 benchmark test functions. The performance and a comparison with CACO and FEP have been studied. 1
New Neural Network Based on Ant Colony Algorithm for Financial Data
- Forecasting,International Conference on Intelligent Information Hiding and Multimedia Signal Processing” ,IEEE computer society,2008
"... Abstract ..."
JMES 50TH ANNIVERSARY ISSUE PAPER 2919 The Bees Algorithm: modelling foraging behaviour to solve continuous optimization problems
, 2008
"... Abstract: The Bees Algorithm models the foraging behaviour of honeybees in order to solve optimization problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This article describes the Bees Algorithm in its basic formulation, and two rec ..."
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Abstract: The Bees Algorithm models the foraging behaviour of honeybees in order to solve optimization problems. The algorithm performs a kind of exploitative neighbourhood search combined with random explorative search. This article describes the Bees Algorithm in its basic formulation, and two recently introduced procedures that increase the speed and accuracy of the search. A critical review of the related swarm intelligence literature is presented. The effective-ness of the proposed method is compared to that of three state-of-the-art biologically inspired search methods. The four algorithms were tested on a range of well-known benchmark func-tion optimization problems of different degrees of complexity. The experimental results proved the reliability of the bees foraging metaphor. The Bees Algorithm performed optimally, or near optimally, in almost all the tests. Compared to the three control algorithms, the Bees Algorithm was highly competitive in terms of learning accuracy and speed. The experimental tests helped also to shed further light on the search mechanisms of the Bees Algorithm and the three control methods, and to highlight their differences, strengths, and weaknesses.
Ant Colony Optimization Algorithm for Continuous Domains Based on Position Distribution Model of Ant Colony Foraging
"... Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process ..."
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Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
First Order Deceptive Problem of ACO and Its Performance Analysis
"... Abstract—Ant colony optimization(ACO), which is one of the intelligential optimization algorithm, has been widely used to solve combinational optimization problems. Deceptive problems have been considered difficult for ant colony optimization. It was believed that ACO will fail to converge to global ..."
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Abstract—Ant colony optimization(ACO), which is one of the intelligential optimization algorithm, has been widely used to solve combinational optimization problems. Deceptive problems have been considered difficult for ant colony optimization. It was believed that ACO will fail to converge to global optima of deceptive problems. This paper proves that the first order deceptive problem of ant colony algorithm satisfies value convergence under certain initial pheromone distribution, but does not satisfy solution convergence. We also present a first attempt towards the value-convergence time complexity analysis of ACO on the first-order deceptive systems taking the n-bit trap problem as the test instance. We prove that time complexity of MMAS, which is an ACO with limitations of the pheromone on each edge, on n-bit trap problem is O(n 2 m.log n), here n is the size of the problem and m is the number of artificial ants. Our experimental results confirm the correctness of our analysis. Index Terms—ant colony optimization, deceptive problems, n-bit trap problem I.
Continuous Optimization Problems
, 2011
"... The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is ..."
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The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication. An Incremental ACOR with Local Search for
