Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> HYBRID METAHEURISTICS <s> Iterated local search (ILS) is a simple and powerful stochastic local search method. This article presents and analyzes the application of ILS to the quadratic assignment problem (QAP). We justify the potential usefulness of an ILS approach to this problem by an analysis of the QAP search space. However, an analysis of the run-time behavior of a basic ILS algorithm reveals a stagnation behavior which strongly compromises its performance. To avoid this stagnation behavior, we enhance the ILS algorithm using acceptance criteria that allow moves to worse local optima and we propose population-based ILS extensions. An experimental evaluation of the enhanced ILS algorithms shows their excellent performance when compared to other state-of-the-art algorithms for the QAP. <s> BIB001 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> HYBRID METAHEURISTICS <s> Nowadays, a promising way to obtain hybrid metaheuristics concerns the combination of several search algorithms with strong specialization in intensification and/or diversification. The flexible architecture of evolutionary algorithms allows specialized models to be obtained with the aim of providing intensification and/or diversification. The outstanding role that is played by evolutionary algorithms at present justifies the choice of their specialist approaches as suitable ingredients to build hybrid metaheuristics. This paper focuses on hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification. We first give an overview of the existing research on this topic, describing several instances grouped into three categories that were identified after reviewing specialized literature. Then, with the aim of complementing the overview and providing additional results and insights on this line of research, we present an instance that consists of an iterated local search algorithm with an evolutionary perturbation technique. The benefits of the proposal in comparison to other iterated local search algorithms proposed in the literature to deal with binary optimization problems are experimentally shown. The good performance of the reviewed approaches and the suitable results shown by our instance allow an important conclusion to be achieved: the use of evolutionary algorithms specializing in intensification and diversification for building hybrid metaheuristics becomes a prospective line of research for obtaining effective search algorithms. <s> BIB002 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> HYBRID METAHEURISTICS <s> The max-min diversity problem (MMDP) consists in selecting a subset of elements from a given set in such a way that the diversity among the selected elements is maximized. The problem is NP-hard and can be formulated as an integer linear program. Since the 1980s, several solution methods for this problem have been developed and applied to a variety of fields, particularly in the social and biological sciences. We propose a heuristic method-based on the GRASP and path relinking methodologies-for finding approximate solutions to this optimization problem. We explore different ways to hybridize GRASP and path relinking, including the recently proposed variant known as GRASP with evolutionary path relinking. Empirical results indicate that the proposed hybrid implementations compare favorably to previous metaheuristics, such as tabu search and simulated annealing. <s> BIB003 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> HYBRID METAHEURISTICS <s> In this paper we propose a new hybrid heuristic for solving 0-1 mixed integer programs based on the principle of variable neighbourhood decomposition search. It combines variable neighbourhood search with a general-purpose CPLEX MIP solver. We perform systematic hard variable fixing (or diving) following the variable neighbourhood search rules. The variables to be fixed are chosen according to their distance from the corresponding linear relaxation solution values. If there is an improvement, variable neighbourhood descent branching is performed as the local search in the whole solution space. Numerical experiments have proven that exploiting boundary effects in this way considerably improves solution quality. With our approach, we have managed to improve the best known published results for 8 out of 29 instances from a well-known class of very difficult MIP problems. Moreover, computational results show that our method outperforms the CPLEX MIP solver, as well as three other recent most successful MIP solution methods. <s> BIB004 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> HYBRID METAHEURISTICS <s> Heuristic manipulation attempts to modify the search space of an optimization problem, using information provided by an underlying heuristic method. In this paper it is applied in combination with tabu search to the fixed spectrum frequency assignment problem. The frequency assignment problem involves the assignment of discrete channels (or frequencies) to the transmitters of a radio network, such as a mobile telephone network. Frequency separation is necessary to avoid interference by other transmitters to the signal received from the wanted transmitter at the reception points. Unnecessary separation causes an excess requirement for spectrum. Good assignments minimize interference and the spectrum required. In fixed spectrum frequency assignment the frequency spectrum available is given and the target is to minimize the interference in the network. Computational experiments confirm that the manipulation technique is able to drive the underlying tabu search algorithm towards improved solutions. <s> BIB005
Over the years, it has become apparent that the need of single traditional metaheuristic has been restricted on the contrary, they are merged with other optimization algorithm called as Hybrid Metaheuristics. The inspiration for hybridization of different algorithm is to accomplish the interrelated behavior of different optimization strategies. There have been following type of hybridization taken place. Hybrid metaheuristic can also be referred from  . Thomas (2006) .A population of solution is used for extracting the useful information which is combined with iterated local search. In order to store their common substructure, where the current solution population member disagree, PBILS keeps small population and enclose the perturbation of iterated local search to subspace. The key postulation of PBILS is that local optimal solution owns common substructure that can be used to enhance the efficiency BIB001 . Besides the two, there are many more techniques of hybridization between metaheuristic and other (meta-) heuristics that was proposed by several researchers. BIB002 proposed a hybrid metaheuristic that contain iterated local search algorithm that uses an evolutionary algorithm as a perturbation technique. The proposed work helps to deal with binary optimization problems BIB002 . Another work include of BIB003 proposed several variants of a hybrid algorithm which is a combination of GRASP and path relinking. The hybridization is carried for maximum minimum diversity problem BIB003 . Another work proposed by Hanafi et al. is a Variable neighborhood decomposition approach (2010) which hybridizes VNS with general purpose CPLEX MIP solver. The hybridization was done to solve 0-1 mixed integer program BIB004 . Glover and Laguna proposed a concept based on the tabu search for improving metaheuristic that is called as a proximate optimality principle (POP). The key assumption of this work is that good solutions have parts in common and they will appear close to each other in the search space  . BIB005 proposed an improved tabu search for solving frequency assignment problem. Hereby, tabu search is enhanced by heuristic manipulation, which is based on adding constraints to a problem so as to reduce the search BIB005 .