Ant Colony Optimization with Adaptive Fitness Function for Satisfiability Testing

Villagra, Marcos; Barán, Benjamín

doi:10.1007/978-3-540-73445-1_26

Cited by 12 publications

(15 citation statements)

References 11 publications

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“…Running for a short time, IROTS [30]-a Tabu search-based algorithm-obtained high-quality solutions unusually quickly, however, it runs out of steam and was outperformed by WALKSAT given sufficient time. Furthermore, WALKSAT is reported to perform at least as well, if not better, than GAs [31], PSO [32], ACO [33], and EDAs [34]. Note that WALKSAT will not get trapped in a local optimum due to its walk steps.…”

Section: A Walksatmentioning

confidence: 99%

Maximum Satisfiability: Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem

Prügel-Bennett

Tayarani-Najaran

2012

IEEE Trans. Evol. Computat.

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Abstract-The fitness landscape of MAX-3-SAT is investigated for random instances above the satisfiability phase transition. This study includes a scaling analysis of the time to reach a local optimum, the number of local optima, the expected probability of reaching a local optimum as a function of its fitness, the expected fitness found by local search and the best fitness, the probability of reaching a global optimum, the size and relative positions of the global optima, the mean distance between the local and global optima, the expected fitness as a function of the Hamming distance from an optimum and their basins of attraction. These analyses show why the problem becomes hard for local search algorithms as the system size increases. The paper also shows how a recently proposed algorithm can exploit long-range correlations in the fitness landscape to improve on the state-of-the-art heuristic algorithms.

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Section: A Walksatmentioning

confidence: 99%

Maximum Satisfiability: Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem

Prügel-Bennett

Tayarani-Najaran

2012

IEEE Trans. Evol. Computat.

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“…The evaluation quality is the number of satisfied clauses multiplied by the weights of these clauses calculated by the weight adaptation heuristic rule [2], [3]. Weight adaptation heuristic is to make the unsolved clauses more important during evaluation each period of time.…”

Section: A Satisfiability Problemmentioning

confidence: 99%

“…To avoid stagnation of the population or falling into local optima, every quantity of pheromone is reduced by a user-defined factor called the evaporation rate [2], [3], [10]. This allows ants to forget the bad assignments.…”

Section: A Satisfiability Problemmentioning

confidence: 99%

“…In practice, nevertheless, there are existing numerous techniques employed generally in computational problems, which can produce substantially faster algorithms: complete and incomplete (stochastic) approaches. One of many problems, Ant Colony Optimization (ACO) has been applied to, is the Boolean Satisfiability (SAT) problem [2]. The SAT problem was the first known example of NPcomplete problems in which the goal is to find an assignment that satisfy a given Boolean formula.…”

Section: Introductionmentioning

confidence: 99%

“…For additional details, we encourage the reader to read the original works of Stützle and Hoos [10]; Moritz and Springer [3]; Villagra and Baran [2].…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Implementation of Ant Colony Optimization on GPU for the Satisfiability Problem

Youness

Ibraheim

Moness

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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This paper focuses on solving the Boolean Satisfiability (SAT) problem using a parallel implementation of the Ant Colony Optimization (ACO) algorithm for execution on the Graphics Processing Unit (GPU) using NVIDIA CUDA (Compute Unified Device Architecture). We propose a new efficient parallel strategy for the ACO algorithm executed entirely on the CUDA architecture, and perform experiments to compare it with the best sequential version exists implemented on CPU with incomplete approaches. We show how SAT problem can benefit from the GPU solutions, leading to significant improvements in speed-up even though keeping the quality of the solution. Our results shows that the new parallel implementation executes up to 21x faster compared to its sequential counterpart.

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An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

Hart

Vlahopoulos

2009

Struct Multidisc Optim

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Ant Colony Optimization with Adaptive Fitness Function for Satisfiability Testing

Cited by 12 publications

References 11 publications

Maximum Satisfiability: Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem

Maximum Satisfiability: Anatomy of the Fitness Landscape for a Hard Combinatorial Optimization Problem

An Efficient Implementation of Ant Colony Optimization on GPU for the Satisfiability Problem

An integrated multidisciplinary particle swarm optimization approach to conceptual ship design

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