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70
MAXMIN Ant System
, 1999
"... Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more finetuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Sa ..."
Abstract

Cited by 122 (3 self)
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Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more finetuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this ares has mainly focused on the development of algorithmic variants which achieve better performance than AS. In this article, we present¨�©� � –¨��� � Ant System, an Ant Colony Optimization algorithm derived from Ant System.¨�©� � –¨��� � Ant System differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to¨� ¨ AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that ¨�©� � –¨��� � Ant System is currently among the best performing algorithms for these problems.
Fitness Landscape Analysis and Memetic Algorithms for the Quadratic Assignment Problem
, 1999
"... In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and metaheuristics based on local search. The local properties of t ..."
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Cited by 84 (9 self)
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In this paper, a fitness landscape analysis for several instances of the quadratic assignment problem (QAP) is performed and the results are used to classify problem instances according to their hardness for local search heuristics and metaheuristics based on local search. The local properties of the tness landscape are studied by performing an autocorrelation analysis, while the global structure is investigated by employing a fitness distance correlation analysis. It is shown that epistasis, as expressed by the dominance of the flow and distance matrices of a QAP instance, the landscape ruggedness in terms of the correlation length of a landscape, and the correlation between fitness and distance of local optima in the landscape together are useful for predicting the performance of memetic algorithms  evolutionary algorithms incorporating local search  to a certain extent. Thus, based on these properties a favorable choice of recombination and/or mutation operators can be found.
A gentle introduction to memetic algorithms
 Handbook of Metaheuristics
, 2003
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Memetic Algorithms for Combinatorial Optimization Problems: Fitness Landscapes and Effective Search Strategies
, 2001
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A Comparison of Memetic Algorithms, Tabu Search, and Ant Colonies for the Quadratic Assignment Problem
 Proc. Congress on Evolutionary Computation, IEEE
, 1999
"... A memetic algorithm (MA), i.e. an evolutionary algorithm making use of local search, for the quadratic assignment problem is presented. A new recombination operator for realizing the approach is described, and the behavior of the MA is investigated on a set of problem instances containing between 25 ..."
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Cited by 38 (4 self)
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A memetic algorithm (MA), i.e. an evolutionary algorithm making use of local search, for the quadratic assignment problem is presented. A new recombination operator for realizing the approach is described, and the behavior of the MA is investigated on a set of problem instances containing between 25 and 100 facilities/locations. The results indicate that the proposed MA is able to produce high quality solutions quickly. A comparison of the MA with some of the currently best alternative approaches  reactive tabu search, robust tabu search and the fast ant colony system  demonstrates that the MA outperforms its competitors on all studied problem instances of practical interest. 1 Introduction The problem of assigning a set of facilities (with given flows between them) to a set of locations (with given distances between them) in such a way that the sum of the product between flows and distances is minimized is known as the facilities location problem [1] or the quadratic assignment ...
Memetic Algorithms for the Traveling Salesman Problem
 Complex Systems
, 1997
"... this paper, the tness landscapes of several instances of the traveling salesman problem (TSP) are investigated to illustrate why MAs are wellsuited for nding nearoptimum tours for the TSP. It is shown that recombination{based MAs can exploit the correlation structure of the landscape. A comparis ..."
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Cited by 37 (8 self)
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this paper, the tness landscapes of several instances of the traveling salesman problem (TSP) are investigated to illustrate why MAs are wellsuited for nding nearoptimum tours for the TSP. It is shown that recombination{based MAs can exploit the correlation structure of the landscape. A comparison of several recombination operators { including a new generic recombination operator { reveals that when using the sophisticated Lin{Kernighan local search, the performance dierence of the MAs is small. However, the most important property of eective recombination operators is shown to be respectfulness. In experiments it is shown that our MAs with generic recombination are among the best evolutionary algorithms for the TSP. In particular, optimum solutions could be found up to a problem size of 3795, and for large instances up to 85,900 cities, nearoptimum solutions could be found in a reasonable amount of time
Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming
, 2000
"... In this paper, a greedy heuristic and two local search algorithms, 1opt local search and kopt local search, are proposed for the unconstrained binary quadratic programming problem (BQP). These heuristics are well suited for the incorporation into metaheuristics such as evolutionary algorithms. Th ..."
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Cited by 33 (3 self)
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In this paper, a greedy heuristic and two local search algorithms, 1opt local search and kopt local search, are proposed for the unconstrained binary quadratic programming problem (BQP). These heuristics are well suited for the incorporation into metaheuristics such as evolutionary algorithms. Their performance is compared for 115 problem instances. All methods are capable of producing high quality solutions in short time. In particular, the greedy heuristic is able to find near optimum solutions a few percent below the bestknown solutions, and the local search procedures are sufficient to find the bestknown solutions of all problem instances with n 100. The kopt local searches even find the bestknown solutions for all problems of size n 250 and for 11 out of 15 instances of size n = 500 in all runs. For larger problems (n = 500; 1000; 2500), the heuristics appear to be capable of finding near optimum solutions quickly. Therefore, the proposed heuristics  especially t...
Ants can solve Constraint Satisfaction Problems
 IEEE Transactions on Evolutionary Computation
, 2001
"... In this paper we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone info ..."
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Cited by 32 (11 self)
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In this paper we describe a new incomplete approach for solving constraint satisfaction problems (CSPs) based on the ant colony optimization (ACO) metaheuristic. The idea is to use artificial ants to keep track of promising areas of the search space by laying trails of pheromone. This pheromone information is used to guide the search, as a heuristic for choosing values to be assigned to variables.
Genetic Algorithms for Binary Quadratic Programming
 in GECCO1999: Proceedings of the Genetic and Evolutionary Computation Conference
, 1999
"... In this paper, genetic algorithms for the unconstrained binary quadratic programming problem (BQP) are presented. It is shown that for small problems a simple genetic algorithm with uniform crossover is sufficient to find optimum or bestknown solutions in short time, while for problems with a high ..."
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Cited by 27 (7 self)
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In this paper, genetic algorithms for the unconstrained binary quadratic programming problem (BQP) are presented. It is shown that for small problems a simple genetic algorithm with uniform crossover is sufficient to find optimum or bestknown solutions in short time, while for problems with a high number of variables (n 200) it is essential to incorporate local search to arrive at highquality solutions. A hybrid genetic algorithm incorporating local search is tested on 40 problem instances of sizes containing between n = 200 and n = 2500. The results of the computer experiments show that the approach is comparable to alternative heuristics such as tabu search for small instances and superior to tabu search and simulated annealing for large instances. New best solutions could be found for 14 large problem instances. 1 INTRODUCTION In the unconstrained binary quadratic programming problem (BQP), a symmetric rational n \Theta n matrix Q = (q ij ) is given, and a binary vector of leng...
DivideandEvolve: a New Memetic Scheme for DomainIndependent Temporal Planning
 LNCS, n o 3906
, 2006
"... Abstract. An original approach, termed DivideandEvolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solution ..."
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Cited by 25 (18 self)
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Abstract. An original approach, termed DivideandEvolve is proposed to hybridize Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the domain of Temporal Planning Problems (TPPs). Whereas standard Memetic Algorithms use local search methods to improve the evolutionary solutions, and thus fail when the local method stops working on the complete problem, the DivideandEvolve approach splits the problem at hand into several, hopefully easier, subproblems, and can thus solve globally problems that are intractable when directly fed into deterministic OR algorithms. But the most prominent advantage of the DivideandEvolve approach is that it immediately opens up an avenue for multiobjective optimization, even though the OR method that is used is singleobjective. Proof of concept approach on the standard (singleobjective) Zeno transportation benchmark is given, and a small original multiobjective benchmark is proposed in the same Zeno framework to assess the multiobjective capabilities of the proposed methodology, a breakthrough in Temporal Planning. 1