نتایج جستجو برای: local search algorithms
تعداد نتایج: 1077840 فیلتر نتایج به سال:
Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. In this case, global optimization techniques are required in order to avoid the local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently ...
Operating rooms have become the most important areas in hospitals because of the scarcity and cost of resources. The present study investigates operating room scheduling and rescheduling considering the priority of surgical patients in a specialized hospital. The ultimate purpose of scheduling is to minimize patient waiting time, surgeon idle time between surgeries, and penalties for deviations...
This paper shows how the performance of evolutionary multiobjective optimization (EMO) algorithms can be improved by hybridization with local search. The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front. On the other hand, the main negative effect is the increase in the computation time per generation. Thus, the number of generations is d...
Guided Local Search (GLS) has been shown to be successful in solving a number of practical real life problems, such as the travelling salesman problem, radio link frequency assignment problem and the vehicle routing problem. GLS is a penalty-based metaheuristic, which works by augmenting the objective function of a local search algorithm with penalties, to help guide them out of local minima. O...
The multiple traveling salesman problem (MTSP) is a generalization of the famous traveling salesman problem (TSP), where more than one salesman is used in the solution. Although the MTSP is a typical kind of computationally complex combinatorial optimization problem, it can be extended to a wide variety of routing problems. This paper presents an efficient and evolutionary optimization algorith...
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