Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Hybridizing Metaheuristic With Constraint Programming <s> We use a local search method we term Large Neighbourhood Search (LNS) to solve vehicle routing problems. LNS is analogous to the shuffling technique of job-shop scheduling, and so meshes well with constraint programming technology. LNS explores a large neighbourhood of the current solution by selecting a number of related customer visits to remove from the set of planned routes, and re-inserting these visits using a constraint-based tree search. Unlike similar methods, we use Limited Discrepancy Search during the tree search to re-insert visits. We analyse the performance of our method on benchmark problems. We demonstrate that results produced are competitive with Operations Research meta-heuristic methods, indicating that constraint-based technology is directly applicable to vehicle routing problems. <s> BIB001 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Hybridizing Metaheuristic With Constraint Programming <s> The securing and sealing of a connector 10 to a pipe 12 for a hydraulic connection comprises a pre-assembly step wherein a cutting/clamping ring 34 is secured to the pipe and a final assembly step wherein the ring 34 is sealed to a connector body 14. The axial force applied by a nut 20 to the ring 34 during the final assembly step is less than the axial force applied to the ring 34 during the pre-assembly step whereby no further cutting of the teeth 40 into the pipe is permitted but sealing takes place between cooperating conical surfaces 22 and 36 of the connector body and cutting/clamping ring respectively. <s> BIB002 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Hybridizing Metaheuristic With Constraint Programming <s> We propose to integrate ACO in a Constraint Programming (CP) language. Basically, we use the CP language to describe the problem to solve by means of constraints and we use the CP propagation engine to reduce the search space and check constraint satisfaction; however, the classical backtrack search of CP is replaced by an ACO search. We report first experimental results on the car sequencing problem and compare different pheromone strategies for this problem. <s> BIB003 </s> Survey of Metaheuristic Algorithms for Combinatorial Optimization <s> Hybridizing Metaheuristic With Constraint Programming <s> Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel constructive search technique that performs a series of resource-limited tree searches where each search begins either from an empty solution (as in randomized restart) or from a solution that has been encountered during the search. A small number of these"elite solutions is maintained during the search. We introduce the technique and perform three sets of experiments on the job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS is carried out to evaluate the performance impact of various parameter settings. Second, we inquire into the diversity of the elite solution set, showing, contrary to expectations, that a less diverse set leads to stronger performance. Finally, we compare the best parameter setting of SGMPCS from the first two experiments to chronological backtracking, limited discrepancy search, randomized restart, and a sophisticated tabu search algorithm on a set of well-known benchmark problems. Results demonstrate that SGMPCS is significantly better than the other constructive techniques tested, though lags behind the tabu search. <s> BIB004
Metaheuristics and constraint programming are two different techniques of optimization. In constraint programming, constrains optimization problem can be either mathematically or symbolic simulated by means of variables , domains and constraints. Constraints summarize the distinct part of problems into sub problems. Each constraint is related to filtering algorithm that remove values which do not put in for feasible solutions. The solutions process of CP has two phases. One is propagation phase and another is labeling phase. In case of break down the search back track. When an optimization problem is solved a bound constraint is carried by placing an improving solution every time. Metaheuristics are efficient in fining a high quality solution but they are not very strong to solve constraint satisfaction problem on the other side CP is extremely efficient in solving decision problems but it is inadequate to tackle an optimization problem. Shaw (1998) proposed a hybrid algorithm containing constraints programming and local search method to solve vehicle routing problem. Basically the approach is to explore a large neighborhood of current solutions by choosing a number of customer visits to take away from routing plan and inserting them using a constraint based tree search BIB001 . Meyer proposed the hybridization of Ant Colony Optimization and constraint programming to solve machine scheduling problem. Hybridization includes the technique for limiting the search performed by an ACO algorithm to promising realms of the search space BIB002 . Another prominent example of the incorporation of metaheuristic and constraint programming was proposed by J. C. BIB004 as solution guided multipoint construction search. It is a novel structural search approach that achieved a sequence of resource limited tree searches where search start either from the vacant solution or from the solution that come across the search some no of selected solution is selected during the search BIB004 . BIB003 proposed a hybrid algorithm that includes ACO and constraint programming to solve car sequencing problem. In this approach, the goal is to find any complete assignment satisfying all the constraints BIB003 .  proposed the integration of evolutionary computation and neural network with constraint programming which result in a hybrid stochastic constraint programming structure that aims to find a policy tree which indicate the decision assignment at any possible situation  .