Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming

Performance of simulated annealing-based heuristic for the unconstrained binary quadratic programming problem

This paper presents a local search algorithm based on variable depth search, called the k-opt local search, for the maximum clique problem. The k-opt local search performs add and drop moves, each of which can be interpreted as 1-opt move, to search a k-opt neighborhood solution at each iteration until no better k-opt neighborhood solution can be found. To evaluate our k-opt local search algorithm, we repeatedly apply the local search for each of DIMACS benchmark graphs and compare with the state-of-the-art metaheuristics such as the genetic local search and the iterated local search reported previously. The computational results show that in spite of the absence of major metaheuristic components, the k-opt local search is capable of finding better (at least the same) solutions on average than those obtained by these metaheuristics for all the graphs.

Performing a local search around solutions found by an evolutionary algorithm is a common practice. Local search is well known to significantly improve the solutions, in particular in the case of combinatorial problems. In this paper a new local search procedure is proposed that uses a locally established utopia point. In the tests in which several instances of the Travelling Salesman Problem (TSP) were solved using an evolutionary algorithm the proposed local search procedure outperformed a local search procedure based on Pareto dominance. Because the local search is focused on improving individual solutions and the multiobjective evolutionary algorithm can improve diversity, various strategies of sharing computational resources between the evolutionary algorithm and the local search are used in this paper. The results attained by the tested methods are compared with respect to computation time, which allows a fair comparison between strategies that distribute computational resources between the evolutionary optimization and the local search in various proportions.

A column generation approach for the unconstrained binary quadratic programming problem

This paper investigates a multi-objective path relinking algorithm in order to optimize a bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the path relinking techniques into hypervolume-based multi-objective optimization, where we propose a method to generate a path and select a set of non-dominated solutions from the generated path for further improvements. Experimental results show that the proposed algorithm is very effective compared with the original multi-objective optimization algorithms.

We present a multi-parent hybrid genetic–tabu algorithm (denoted by GTA) for the Unconstrained Binary Quadratic Programming (UBQP) problem, by incorporating tabu search into the framework of genetic algorithm. In this paper, we propose a new multi-parent combination operator for generating offspring solutions. A pool updating strategy based on a quality-and-distance criterion is used to manage the population. Experimental comparisons with leading methods for the UBQP problem on 25 large public instances demonstrate the efficacy of our proposed algorithm in terms of both solution quality and computational efficiency.KeywordsUBQPMemetic AlgorithmTabu SearchGenetic Algorithmmulti-parent combination

Recently, inspired by quantum annealing, many solvers specialized for unconstrained binary quadratic programming problems have been developed. For further improvement and application of these solvers, it is important to clarify the differences in their performance for various types of problems. In this study, the performance of four quadratic unconstrained binary optimization problem solvers, namely D-Wave Hybrid Solver Service (HSS), Toshiba Simulated Bifurcation Machine (SBM), Fujitsu Digital Annealer (DA), and simulated annealing on a personal computer, was benchmarked. The problems used for benchmarking were instances of real problems in MQLib, instances of the SAT-UNSAT phase transition point of random not-all-equal 3-SAT (NAE 3-SAT), and the Ising spin glass Sherrington-Kirkpatrick (SK) model. Concerning MQLib instances, the HSS performance ranked first; for NAE 3-SAT, DA performance ranked first; and regarding the SK model, SBM performance ranked first. These results may help understand the strengths and weaknesses of these solvers.

MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

Multi-objectivization inspired metaheuristics for the sum-of-the-parts combinatorial optimization problems

Landing aircraft safely is an important operation that air traffic controllers have to deal with on a daily basis. For each arriving aircraft a runway and a landing time must be allocated. If these allocations can be done in an efficient way, it could give the airport a competitive advantage. The Aircraft Landing Problem (ALP) aims to minimize the deviation from a preferred target time of each aircraft. It is an NP-hard problem, meaning that we may have to resort to heuristic methods as exact methods may not be suitable, especially as the problem size increases. This paper proposes an iterated local search (ILS) algorithm for the ALP. ILS is a single solution based search methodology that successively invokes a local search procedure to find a local optimum solution. A perturbation operator is used to modify the current solution in order to escape from the local optimum and to provide a new solution for the local search procedure. As different problems and/or instances have different characteristics, the success of the ILS is highly dependent on the local search, the perturbation operator(s) and the perturbation strength. To address these issues, we utilize four perturbation operators and a time varying perturbation strength which changes as the algorithm progresses. A variable neighborhood descent algorithm is used as our local search. The proposed ILS generates high quality solutions for the ALP benchmark instances taken from the scientific literature, demonstrating its efficiency in terms of both solution quality and computational time. Moreover, the proposed ILS produces new best results for some instances.

Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search

A large neighborhood search algorithm and lower bounds for the variable-Sized bin packing problem with conflicts

Local search for weighted sum coloring problem

A LOCAL SEARCH ALGORITHM FOR THE INVENTORY MATCHING PROBLEM IN A STEEL COMPANY

The discounted knapsack problem (DKP) is an NP-hard combinatorial optimization problem that has gained much attention recently. Due to its high complexity, the usual solution combines a global search algorithm with a greedy local search algorithm to repair candidate solutions. The current greedy algorithms use a heuristic that ignores the items already in a candidate solution. This paper presents a new greedy algorithm for DKP that uses an expanded set of operators and better heuristics that are more effective at considering the selected items. Experimental results show that the proposed greedy algorithm has superior performance to three well-known greedy algorithms for DKP, both when operating independently and when combined with global search algorithms.KeywordsCombinatorial optimizationDiscounted knapsack problemGreedy algorithmLocal search