metaheuristic


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metaheuristic

(algorithm, complexity, computability)
A top-level general strategy which guides other heuristics to search for feasible solutions in domains where the task is hard.

Metaheuristics have been most generally applied to problems classified as NP-Hard or NP-Complete by the theory of computational complexity. However, metaheuristics would also be applied to other combinatorial optimisation problems for which it is known that a polynomial-time solution exists but is not practical.

Examples of metaheuristics are Tabu Search, simulated annealing, genetic algorithms and memetic algorithms.
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The rest of paper is organized as follows: Section 2 defines the two approaches for the evaluation of the objective function values for the VRPSD; Section 3 explains the metaheuristic GA; Section 4 discusses the computational results and finally, Section 5 shows some concluding remarks of this research.
Comparison efficiency indicators of routing strategies for picking PLPC Tabu search metaheuristic Route distance picking 37,5 m 21 m Time/Route 375 seconds 210 seconds Costs/route $ 6250 $ 3500
Minimizing the end-to-end delay in Unicast networks using metaheuristic techniques
Along these lines, an enhancement of the convergence in terms of the number of iterations within the metaheuristic procedure and, consequently, a reduction of the total computational time required to obtain a converged solution of problem can be achieved.
develop mathematical programming models and metaheuristic algorithms to optimize emerging WEEE collection systems, (7) identifying the key strategic decision areas which impact the operation of WEEE collection systems, and addressing the problem of designing routes for the collection of WEEE with a fixed and heterogeneous fleet of capacitated vehicles.
CMAES is an evolution-strategy based metaheuristic algorithm.
The authors performed two computational experiments: the first was the evaluation of deterministic heuristic methods; and the second was the evaluation of metaheuristic methods.
To solve the loading schedule problem we used two metaheuristic techniques that have already proven as efficient methods for solving combinatorial optimization problems.
First of all, the CS algorithm is a nature inspired metaheuristic algorithm and is applicable to a wider class of optimization problems.
Therefore, optimization has migrated from deterministic methods, to evolutionary and metaheuristic ones, such as PSO.