Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for b...

Abstract The distribution–allocation problem is known as one of the most comprehensive strategic decisions. In real-world cases, it impossible to solve a completely in acceptable time. This forces researchers develop efficient heuristic techniques for large-term operation whole supply chain. These provide near optimal solution and are comparably fast particularly large-scale test problems. pape...

This paper demonstrates dynamical system models of genetic algorithms that exhibit cycling and chaotic behavior. The genetic algorithm is a binary-representation genetic algorithm with truncation selection and a density-dependent mutation. The density dependent mutation has a separate mutation rate for each bit position which is a function of the level of convergence at that bit position. Densi...

Uniform crossover for binary strings has a natural geometric interpretation that allows us to generalize it rigorously to any search space endowed with a notion of distance and any representation [6]. In this paper, we present an analogous characterization for one-point crossover and explicitly derive formally specific one-point crossovers for a number of well-known representations.

This paper documents our investigation into various heuristic methods to solve the vehicle routing problem with time windows (VRPTW) to near optimal solutions. The objective of the VRPTW is to serve a number of customers within prede®ned time windows at minimum cost (in terms of distance travelled), without violating the capacity and total trip time constraints for each vehicle. Combinatorial o...

Genetic algorithms (GA) are instances of random heuristic search (RHS) which mimic biological evolution and molecular genetics in simplified form. These random search algorithms can be theoretically described with the help of a deterministic dynamical system model by which the stochastic trajectory of a population can be characterized using a deterministic heuristic function and its fixed point...

Genetic Algorithm (GAs) is used to solve optimization problems. It is depended on the selection operator, crossover and mutation rates. In this paper Roulette Wheel Selection (RWS) operator with different crossover and mutation probabilities, is used to solve well known optimization problem Traveling Salesmen Problem (TSP). We have compared the results of RWS with another selection method Stoch...

Iterated local search (ILS) is a powerful meta-heuristic algorithm applied to a large variety of combinatorial optimization problems. Contrary to evolutionary algorithms (EAs) ILS focuses only on a single solution during its search. EAs have shown however that there can be a substantial gain in search quality when exploiting the information present in a population of solutions. In this paper we...

Cloud computing is an emerging technology and it allows users to pay as you need and has the high performance. Cloud computing is a heterogeneous system as well and it holds large amount of application data. In the process of scheduling some intensive data or computing an intensive application, it is acknowledged that optimizing the transferring and processing time is crucial to an application ...

real coded genetic algorithm, rcga, is the type of ga which operates on chromosomes with real valued parameters. different mutation and crossover operations are defined for rcga. one usable crossover for this kind of ga is to consider its chromosomes simply as bit strings and utilize the same operations as binary coded ga. in this paper, we attempt to show that this kind of crossover can not ha...