The multiobjective unconstrained binary quadratic programming problem is an important combinatorial optimization problem with both theory and practical values. Until now, several efforts have been made to design metah...
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The multiobjective unconstrained binary quadratic programming problem is an important combinatorial optimization problem with both theory and practical values. Until now, several efforts have been made to design metaheuristic methods to solve the problem. However, designing such effective methods is not trivial and heavily depends on experts' specific knowledge. Meanwhile, due to the iterative nature of metaheuristic methods, they require a long time to find high-quality solutions. From the perspective of machine learning, this paper proposes a deep reinforcement learning method to solve the problem. The method can automatically learn effective heuristics from a large amount of data, thus decreasing the need for experts' knowledge. Meanwhile, by leveraging the power of GPU, the method can quickly obtain high-quality solutions for a batch of instances. Experimental results show the proposed method outperforms two classical metaheuristic methods in terms of solution quality and running time for solving the problem.
unconstrained binary quadratic programming (UBQP) problem plays an important role in operational research due to its application potential and its computational challenge. This paper presents a new hybrid algorithm ba...
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ISBN:
(纸本)9781665440899
unconstrained binary quadratic programming (UBQP) problem plays an important role in operational research due to its application potential and its computational challenge. This paper presents a new hybrid algorithm based on Harmony Search (HS) and Teaching-Learning-Based Optimization. The main features of the proposed algorithm called harmony search with teaching-learning (HSTL) are the integration of teaching-learning strategy in the basic harmony search. This hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the Teaching-Learning-Based Optimization. Experiments on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.
The unconstrained binary quadratic programming problem (UBQP) belongs to the NP-hard class and has become a framework for modeling a variety of combinatorial optimization problems. The methods most commonly used to so...
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The unconstrained binary quadratic programming problem (UBQP) belongs to the NP-hard class and has become a framework for modeling a variety of combinatorial optimization problems. The methods most commonly used to solve instances of the UBQP explore the concept of neighborhood of a solution. Given a binary vector x is an element of{0, 1}(n), solution to a UBQP instance, a neighborhood of x can be defined by flip moves. Flip moves consist on selecting one or more elements (positions) of x and "flip" their values to their complementary values (i.e., from 1 to 0 or from 0 to 1). Normally, those methods compute a large number of flip moves, and so the whole process to solve an instance can be quite time consuming. In order to reduce this time, some works have proposed ways to efficiently evaluate one or two flip moves, and also extensions to higher order moves. In this paper we propose two closed-form formulas for evaluating quickly any order of flip moves. To test our theoretical findings, we executed an extensive set of computational experiments over well-known instances for the problem. Against common belief, our results show that it is possible to compute high order flip moves very fast. (C) 2019 Elsevier Ltd. All rights reserved.
unconstrained binary quadratic programming(UBQP) problem plays an important role in operational research due to its application potential and its computational *** paper presents a new hybrid algorithm based on Harmon...
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unconstrained binary quadratic programming(UBQP) problem plays an important role in operational research due to its application potential and its computational *** paper presents a new hybrid algorithm based on Harmony Search(HS) and Teaching-Learning-Based *** main features of the proposed algorithm called harmony search with teaching-learning(HSTL) are the integration of teaching-learning strategy in the basic harmony *** hybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the Teaching-Leaming-Based *** on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.
Local search is known to be a highly effective metaheuristic framework for solving a number of classical combinatorial optimization problems, which strongly depends on the characteristics of neighborhood structure. In...
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ISBN:
(纸本)9789811064425;9789811064418
Local search is known to be a highly effective metaheuristic framework for solving a number of classical combinatorial optimization problems, which strongly depends on the characteristics of neighborhood structure. In this paper, we integrate the neighborhood combination strategies into the hypervolume-based multi-objective local search algorithm, in order to deal with the bi-objective unconstrained binary quadratic programming problem. The experimental results show that certain combinations are superior to others. The performance analysis sheds lights on the ways to further improvements.
This paper presents a multi-objective backbone guided search algorithm in order to optimize a bi-objective unconstrained binary quadratic programming problem. Our proposed algorithm consists of two main procedures whi...
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ISBN:
(纸本)9783319422947;9783319422930
This paper presents a multi-objective backbone guided search algorithm in order to optimize a bi-objective unconstrained binary quadratic programming problem. Our proposed algorithm consists of two main procedures which are hypervolume-based local search and backbone guided search. When the hypervolume-based local search procedure can not improve the Pareto approximation set any more, the backbone guided search procedure is applied for further improvements. Experimental results show that the proposed algorithm is very effective compared with the original multi-objective optimization algorithms.
The conventional unconstrained binary quadratic programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective...
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The conventional unconstrained binary quadratic programming (UBQP) problem is known to be a unified modeling and solution framework for many combinatorial optimization problems. This paper extends the single-objective UBQP to the multiobjective case (mUBQP) where multiple objectives are to be optimized simultaneously. We propose a hybrid metaheuristic which combines an elitist evolutionary multiobjective optimization algorithm and a state-of-the-art single-objective tabu search procedure by using an achievement scalarizing function. Finally, we define a formal model to generate mUBQP instances and validate the performance of the proposed approach in obtaining competitive results on large-size mUBQ Pinstances with two and three objectives. (C) 2013 Elsevier B. V. All rights reserved.
The unconstrained binary quadratic programming is known to be NP-hard, and is a unified model for a lot of combinatorial optimization problems. This paper presents a binary particle swarm optimization for solving the ...
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ISBN:
(纸本)9781845648442;9781845648435
The unconstrained binary quadratic programming is known to be NP-hard, and is a unified model for a lot of combinatorial optimization problems. This paper presents a binary particle swarm optimization for solving the unconstrained binary quadratic programming. The proposed algorithm adopts a method to update position, and uses mutation operation to produce new solutions. Then, the new solutions are refined by a local search procedure. The algorithm was tested on a benchmark set from the literature. The experimental results show that the proposed algorithm is able to find high-quality solutions within an acceptable runtime.
A Tabu Search heuristic for unconstrained binary quadratic programming performs perfectly on a range of random problem instances. A genetic algorithm searches spaces of UBQP instances for instances that challenge the ...
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ISBN:
(纸本)9781450311786
A Tabu Search heuristic for unconstrained binary quadratic programming performs perfectly on a range of random problem instances. A genetic algorithm searches spaces of UBQP instances for instances that challenge the heuristic. The GA's evaluation step compares the performance of the Tabu Search to that of a memetic algorithm on the candidate stance being evaluated. On UBQP instances evolved by the GA, the TS heuristic returns solutions that are inferior to those of the memetic algorithm by significant margins.
Pareto local search (PLS) is a basic building block in many metaheuristics for a multiobjective combinatorial optimization problem. In this paper, an enhanced PLS variant called parallel PLS based on decomposition (PP...
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Pareto local search (PLS) is a basic building block in many metaheuristics for a multiobjective combinatorial optimization problem. In this paper, an enhanced PLS variant called parallel PLS based on decomposition (PPLS/D) is proposed. PPLS/D improves the efficiency of PLS using the techniques of parallel computation and problem decomposition. It decomposes the original search space into L subregions and executes L parallel processes searching in these subregions simultaneously. Inside each subregion, the PPLS/D process is guided by a unique scalar objective function. PPLS/D differs from the well-known two phase PLS in that it uses the scalar objective function to guide every move of the PLS procedure in a fine-grained manner. In the experimental studies, PPLS/D is compared against the basic PLS and a recently proposed PLS variant on the multiobjective unconstrained binary quadratic programming problems and the multiobjective traveling salesman problems with, at most, four objectives. The experimental results show that regardless of whether the initial solutions are randomly generated or generated by heuristic methods, PPLS/D always performs significantly better than the other two PLS variants.
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