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We propose an adaptive large neighborhood search to solve this problem.
We propose algorithms based on uniform quantization and sequential search to solve the corresponding non-convex optimization problems.
Formulating the problem based on flexibility analysis, we apply a meta-heuristic method termed hybrid tabu search to solve the problem.
This paper reports our efforts in designing an object-oriented heuristic framework that integrates semi-greedy growth and local search to solve a real-world applied p-compact-regions problem to optimality or near-optimality.
This model uses a clustering algorithm to divide a given network into several small clusters with a manageable number of nodes then separately use hybrid heuristic algorithm based on Genetic Algorithm and Tabu Search to solve each cluster.
In this paper, we use tabu search to solve this feature selection problem and compare it with classic algorithms, such as sequential methods, branch and bound method, etc., and most other suboptimal methods proposed recently, such as genetic algorithm and sequential forward (backward) floating search methods.
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THE MUSIC OF THE PRIMES Searching to Solve the Greatest Mystery in Mathematics.
Short Al's silence led to a long period of searching to solve the mystery.
And then, we propose an EE-Max algorithm coupled with the Max-SINR, nonlinear fraction programming and one dimension searching to solve the reformulated problem.
As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.
In this study, we propose a binary differential search method to solve 0 1 MKPs where the stochastic search is guided by a Brownian motion-like random walk.
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