Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This me...

The travelling salesman problem (TSP) is the most well-known combinatorial optimization problem. TSP is used to find a routing of a salesman who starts from a home location, visits a prescribed set of cities and returns to the original location in such a way that the total distance travelled is minimized and each city is visited exactly once . This problem is known to be NP-hard, and cannot be ...

This paper contains an experimental study of the impact of the construction strategy of reduced, ordered binary decision diagrams (ROBDDs) on the average-case computational complexity of random 3-SAT, using the CUDD package. We study the variation of median running times for a large collection of random 3-SAT problems as a function of the density as well as the order (number of variables) of th...

background: acute appendicitis is one of the common and urgent illnesses among children.  children usually are unable to help the physicians completely due to weakness in describing the medical history. moreover, acute appendicitis overlaps with conditions of other diseases in terms of symptoms and signs in the first hours of presentation. these conditions lead to unwanted biases as well as err...

The travelling salesman problem (TSP) is the most well-known combinatorial optimization problem. TSP is used to find a routing of a salesman who starts from a home location, visits a prescribed set of cities and returns to the original location in such a way that the total distance travelled is minimized and each city is visited exactly once . This problem is known to be NP-hard, and cannot be ...

Differential evolution (DE),as one of the evolutionary algorithms, has recently been employed to optimize water distribution systems (WDSs). Several parameters need to be determined in the use of DE, including: population size, N; mutation weighting factor, F; and crossover rate, CR. The search behaviour of DE is governed by these three parameters. The objective of this paper is to investigate ...

The paper presents Discrete PSO algorithm for document clustering problems. This algorithm is hybrid of PSO with GA operators. The proposed system is based on population-based heuristic search technique, which can be used to solve combinatorial optimization problems, modeled on the concepts of cultural and social rules derived from the analysis of the swarm intelligence (PSO) with GA operators ...

The use of the term \landscape" is increasing rapidly in the eld of evolutionary computation, yet in many cases it remains poorly, if at all, deened. This situation has perhaps developed because everyone grasps the imagery immediately, and the questions that would be asked of a less evocative term do not get asked. This paper presents an important consequence of a new model of landscapes. The m...

A novel hybrid evolutionary algorithm is developed based on the particle swarm optimization (PSO) and genetic algorithms (GAs). The PSO phase involves the enhancement of worst solutions by using the global-local best inertia weight and acceleration coefficients to increase the efficiency. In the genetic algorithm phase, a new rank-based multi-parent crossover is used by modifying the crossover ...

A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS)...