▇▇▇▇ Search Sample Clauses
▇▇▇▇ Search. ▇▇▇▇ Search is another type of heuristic algorithm. Whereas a GA performs a global search, a ▇▇▇▇ search algorithm makes more use of the local region of the design space [13]. It works as follows: • An initial solution is randomly generated and evaluated. • Next, all neighbouring solutions - i.e., those that can be obtained by making a single modification to the initial solution - are generated and evaluated. • The neighbour with the best fitness is chosen as the next solution, and the process repeats. In this way, ▇▇▇▇ search deterministically moves through the design space. To prevent it from looping or getting stuck, it also maintains a list of forbidden or 'taboo' moves, which is populated by recently tried solutions. This helps force the algorithm to search in new, unexplored regions, although the list is finite and so it is possible to loop back some time later. The whole process is repeated until either a set number of moves have been made or a solution is found for which no better solutions are available. Modifications to the algorithm are also possible, e.g. by using an adaptive penalty function similar to that found in penalty-based GAs. ▇▇▇▇ search can be more efficient than GAs in some circumstances [13], requiring less evaluation, but they can also be more prone to becoming stuck in local optima. Variations on the algorithm exist, e.g. to offer better support for multiple objectives; one such approach randomly selects one of multiple objectives to be active at each stage [12], giving a more equal balance to the multiple objectives and exploring more of the search space. In this approach, a set of good solutions is remembered; at each stage, the best newly discovered solutions are added, and any dominated solutions in the resultant set are discarded. This produces a Pareto front. To ensure an even spread of the Pareto front over the search space, a diversification feature can be added wherein the algorithm can randomly select another Pareto solution to be used as the starting point if no new solutions have been discovered for a set number of iterations.
▇▇▇▇ Search. The implemented ▇▇▇▇ search procedure is based on Dr. ▇▇▇▇▇▇▇▇▇▇’s description of his ▇▇▇▇ search for the IPDP, but some improvements in the search procedure have been implemented. The improvements and differences described in this thesis compared to Dr. ▇▇▇▇▇▇▇▇▇▇’s description is stated in the method description. The ▇▇▇▇ search procedure described in this thesis also features a parallelized neighborhood exploration and parallelized post-optimization procedures.
▇▇▇▇ Search. Seller shall provide Purchaser a tax lien search and financing statement search, both certified to a date later than the Effective Date.
▇▇▇▇ Search. The Administrative Agent shall have received the results of customary Lien searches (including UCC and PPSA searches and a search as to bankruptcy, tax and intellectual property matters as applicable), in form and substance reasonably satisfactory thereto, made against the Credit Parties, indicating among other things that the assets of each such Credit Party are free and clear of any Lien (except for Permitted Liens).
▇▇▇▇ Search. ▇▇▇▇ search introduced by ▇▇▇▇ ▇▇▇▇▇▇ in 1986, is a metaheuristic which applies the principles of a local search to find optimal solutions to an optimization problem by cutting out parts of the search from the search space using some guidance. At every iteration (t), the ▇▇▇▇ search moves from a current solution (s) to the best solution in its neighbourhood N(st). The choice of neighbourhood is selected by a given neighbourhood operator, the neighbours in the search space are evaluated and checked for feasibility. The best feasible move in the neighbourhood is selected based on a criterion e.g. best improvement, first improvement. As a metaheuristic, ▇▇▇▇ search escapes local optima by accepting worsening moves. The word ▇▇▇▇ was coined by ▇▇▇▇▇▇ as certain attributes of a move are made ▇▇▇▇ i.e. moves with those attributes are not allowed. This actually prevents solutions from being revisited and also solutions with the attribute from being visited. Such moves remain ▇▇▇▇ for a certain duration referred to as the ▇▇▇▇ tenure. It is likely that a good solution can be made ▇▇▇▇ due an attribute. In order to allow such good solutions to be selected, an aspiration criterion may be included / implemented to allow such solutions to be visited if only it gives a best objective so far in the search and has not been visited previously. Other properties which could be added are diversification and intensification. Diversification ensures that more of the solution space is visited and intensification is used to search more within a promising part of the solution space[12]. ▇▇▇▇ search can therefore be considered as a good improvement heuristic for solving the VRP/IRP for NorStone in the project “Ny logistikk løsning for NorStone” which is being carried out by Møreforskning AS and Molde University College.