This paper browses through some well-known meta-heuristic search strategies, and briefly discusses some of their recent applications to the VLSI layout design process. It starts with very brief description of the different phases of VLSI layout design, and a brief overview of Meta-Heuristic search strategies. Since most of the VLSI layout design problems are hard optimization problems, the conc...

Load shedding has been extensively studied because of multiple power system failure occurrences worldwide. Reliable techniques are required to provide rapid and precise load shedding to avert voltage collapse in power networks. Meta-heuristic optimization approaches are currently the widely developed methods because of their robustness and flexibility in dealing with complex and non-linear syst...

Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. In this paper we put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief ove...

the determination of the ultimate pit limit (upl) is the first step in the open pit mine planning process. in this stage that parts of the mineral deposit that are economic to mine are determined. there are several mathematical, heuristic and meta-heuristic algorithms to determine upl. the optimization criterion in these algorithms is maximization of the total profit whilst satisfying the...

An emerging, yet extremely important topic in simulation application and research is simulation optimization. Emphasis in the literature, in professional conferences, and particularly in commercial software packages underscore the importance of simulation optimization. We address this topic in a more general, model optimization venue. In particular we describe the use of a genetic algorithm in ...

This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the ev...

Deep learning (DL) is a type of machine learning that mimics the thinking patterns of a human brain to learn the new abstract features automatically by deep and hierarchi‐ cal layers. DL is implemented by deep neural network (DNN) which has multihidden layers. DNN is developed from traditional artificial neural network (ANN). However, in the training process of DL, it has certain inefficiency d...

location-allocation is a combinatorial optimization problem, and is defined as non deterministic polynomial hard (np) hard optimization. therefore, solution of such a problem should be shifted from exact to heuristic or meta heuristic due to the complexity of the problem. locating medical centers and allocating injuries of an earthquake to them has high importance in earthquake disaster managem...

The vehicle routing problem (VRP), a well-known combinatorial optimization problem, holds a central place in logistics management. Many meta-heuristic approaches like Simulated Annealing (SA), Genetic Algorithms (GA), Tabu Search (TS), and Ant Colony Optimization (ACO) have been proposed to solve VRP. Ant Algorithm is a distributed meta-heuristic approach that has been applied to various combin...