This paper presents a hyper heuristic that is able to adapt two low level parameters (depth of search rate and mutation intensity) and the probability of applying a low level heuristic to an evolving solution in order to improve the solution quality. Basically, two random subsets of heuristics are maintained: one sub set of the full set of heuristics and other sub-set of the local heuristics. F...

The traveling salesman problem (TSP) is one of the most important combinational optimization problems that have nowadays received much attention because of its practical applications in industrial and service problems. In this paper, a hybrid two-phase meta-heuristic algorithm called MACSGA used for solving the TSP is presented. At the first stage, the TSP is solved by the modified ant colony s...

This study is devoted to discrete sizing optimization of truss structures employing an efficient discrete evolutionary meta-heuristic algorithm which uses the Newton gradient-based method as its updating scheme and it is named here as Newton Meta-heuristic Algorithm (NMA). In order to enable the NMA population-based meta-heuristic to effectively explore the discrete design space, a term contain...

Abstract Many algorithms for maximizing a monotone submodular function subject to knapsack constraint rely on the natural greedy heuristic. We present novel refined analysis of this heuristic which enables us to: (1) reduce enumeration in tight ( 1 ? e ) -approximation [Sviridenko 04] from subsets size three two; (2) an improved upper bound 0.42945 classic algorithm returns better between singl...

in recent years, wind energy has a remarkable growth in the world, but one of the important problems of power generated from wind is its uncertainty and corresponding power. for solving this problem, some approaches have been presented. recently, the artificial neural networks (ann) as a heuristic method has more applications for this propose. in this paper, short-term (1 hour) and mid-term (24...