Combining simulated annealing with local search heuristic for MAX-SAT

Bouhmala, Noureddine

doi:10.1007/s10732-018-9386-9

Cited by 14 publications

(2 citation statements)

References 38 publications

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“…Finally, it reaches the ground state at room temperature, and the internal energy is minimized. 32 The algorithm accepts the solution with poor fitness at a certain probability in the search space through the Metropolis criteria. So as to maintain the population diversity, jump out of the local optimal trap, and finally reach the internal energy at room temperature, namely the objective function value.…”

Section: Hybrid Particle Swarm Optimization Methodsmentioning

confidence: 99%

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Chen

Zou

Wang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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To solve the problems of low efficiency and insufficient dynamic response of job shop scheduling in the discrete manufacturing process, a multi-objective flexible job shop scheduling model for digital twin and its solution method are proposed. Firstly, a digital twin scheduling model with physical entity, virtual model and production plan is constructed, and four factors are taken as optimization goals. Then, a hybrid particle swarm optimization method is designed to increase the refined optimization ability, and the obtained Pareto optimal solution set is analyzed by grey relational analysis to obtain a satisfactory solution which coincides with the actual production. Finally, a three-dimensional model which is completely mapped with the real job shop scheduling is built by Plant Simulation software. The scheduling process is simulated and optimized by combining with the production data of an enterprise, which verifies the feasibility and applicability of this method, and will effectively guide the production practice.

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Section: Hybrid Particle Swarm Optimization Methodsmentioning

confidence: 99%

Digital twin oriented multi-objective flexible job shop scheduling model and its hybrid particle swarm optimization

Chen

Zou

Wang

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Finally, many other SLS algorithms have been applied to the SAT. These include techniques such as Simulated Annealing [68,69], Evolutionary Algorithms [70], and Greedy Randomized Adaptive Search Procedures [71]. The nature-inspired GASAT algorithm [72] is a hybrid algorithm that combines a specific crossover and a tabu search procedure.…”

Section: Stochastic Local Search Algorithms (Sls)mentioning

confidence: 99%

A team of pursuit learning automata for solving deterministic optimization problems

2020

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Learning Automata (LA) is a popular decision-making mechanism to "determine the optimal action out of a set of allowable actions" [1]. The distinguishing characteristic of automata-based learning is that the search for an optimal parameter (or decision) is conducted in the space of probability distributions defined over the parameter space, rather than in the parameter space itself [2]. In this paper, we propose a novel LA paradigm that can solve a large class of deterministic optimization problems. Although many LA algorithms have been devised in the literature, those LA schemes are not able to solve deterministic optimization problems as they suppose that the environment is stochastic. In this paper, our proposed scheme can be seen as the counterpart of the family of pursuit LA developed for stochastic environments [3]. While classical pursuit LAs can pursue the action with the highest reward estimate, our pursuit LA rather pursues the collection of actions that yield the highest performance by invoking a team of LA. The theoretical analysis of the pursuit scheme does not follow classical LA proofs, and can pave the way towards more schemes where LA can be applied to solve deterministic optimization problems. Furthermore, we analyze the scheme under both a constant learning parameter and a time-decaying learning parameter. We provide some experimental results that show how our Pursuit-LA scheme can be used to solve the Maximum Satisfiability (Max-SAT) problem. To avoid premature convergence and better explore the search space, we enhance our scheme with the concept of artificial barriers recently introduced in [4]. Interestingly, although our scheme is simple by design, we observe that it performs well compared to sophisticated state-of-the-art approaches.

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