An iterative Path-Breaking approach with mutation and restart strategies for the MAX-SAT problem

Xu, Zhenxing; Li, Chu-Min

doi:10.1016/j.cor.2018.12.005

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…Local search algorithms usually use some perturbation methods to diversify the solution when meeting the local optimal (Cai, Luo, and Zhang 2017;Xu, He, and Li 2019;Wang et al 2020a,b). In the solution restart phase of our algorithm, an important component is called history information perturbation mechanism (P erturb), which restarts the algorithm by constructing a new solution based on previous history information when falling into the local optima.…”

Section: The Perturb Functionmentioning

confidence: 99%

A Fast Local Search Algorithm for the Latin Square Completion Problem

Pan

Wang

Yin

2022

AAAI

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The Latin square completion (LSC) problem is an important NP-complete problem with numerous applications. Given its theoretical and practical importance, several algorithms are designed for solving the LSC problem. In this work, to further improve the performance, a fast local search algorithm is developed based on three main ideas. Firstly, a reduction reasoning technique is used to reduce the scale of search space. Secondly, we propose a novel conflict value selection heuristic, which considers the history conflicting information of vertices as a selection criterion when more than one vertex have equal values on the primary scoring function. Thirdly, during the search phase, we record previous history search information and then make use of these information to restart the candidate solution. Experimental results show that our proposed algorithm significantly outperforms the state-of-the-art heuristic algorithms on almost all instances in terms of success rate and run time.

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Section: The Perturb Functionmentioning

confidence: 99%

A Fast Local Search Algorithm for the Latin Square Completion Problem

Pan

Wang

Yin

2022

AAAI

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“…where S i 1 is the neuron state where i 1 ∈ {1, −1}. The probability for consistent interpretation is expressed in (11).…”

Section: Random K Satisfiability In Discrete Hopfield Neural Network ...mentioning

confidence: 99%

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

et al. 2022

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Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research.

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Stochastic local search for Partial Max-SAT: an experimental evaluation

Alkasem

Menaï

2020

Artif Intell Rev

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An iterative Path-Breaking approach with mutation and restart strategies for the MAX-SAT problem

Cited by 9 publications

References 21 publications

A Fast Local Search Algorithm for the Latin Square Completion Problem

A Fast Local Search Algorithm for the Latin Square Completion Problem

A Novel Multi-Objective Hybrid Election Algorithm for Higher-Order Random Satisfiability in Discrete Hopfield Neural Network

Stochastic local search for Partial Max-SAT: an experimental evaluation

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