In computer-aided logic design, partitioning is the task of clustering circuit elements into groups so that a given objective function is optimized with respect to a set of design constraints. In this paper we present a genetic algorithm for the circuit partitioning problem. The objective of the proposed algorithm is not only to balance the size of the two portions, but also to evenly distribut...

Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...

Simulated Annealing and Genetic Algorithm are two well-known metaheuristic algorithms for combinatorial optimization. These two methods have also been used for solving constrained continuous problems. In this study, five constrained continuous problems have been solved both Simulated Annealing (SA) and Genetic Algorithm (GA). Optimum results have been compared with real optimum values obtained ...

Template learning has potential application in several areas of Cellular Neural Network research, including texture recognition, pattern detection and so on. In this letter, a recently-developed algorithm called Adaptive Simulated Annealing is investigated for learning CNN templates, as a superior alternative to the Genetic Algorithm. key words: adaptive simulated annealing, cellular neural net...

This paper proposes Parallel Simulated Annealing using Genetic Crossover (PSA/GAc). In this algorithm, there are several processes of Simulated Annealing (SA) working parallel. To exchange information between the solutions, the operation of genetic crossover is performed. Through the continuous test problems, it is found that PSA/GAc can search the solution effectively. The proposed algorithm i...

Quadratic Assignment Problem (QAP) is an NPhard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a real-life (QAP) and analyze their performance in terms of both runtime effici...