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In the paper, the problem of efficient task allocation in torus mesh network is considered. The authors tested the implemented metaheuristic algorithm which is based on Differential Evaluation method. The focus is taken on tuning the algorithm, i.e., choosing the best parameters of mutation scheme. The research was made using the new designed and implemented experimentation system. Ten mutation schemes...
Swarm intelligence systems are basically made up simple agent's populations which are interacting locally with each other and with their surroundings. These agents local interaction with each other can be negative, positive or neutral. Here positive interaction helps agents to solve a problem while negative interaction block the agents for solving problem. swarm's performance does not affected by...
The experimental data is soybean gene variation information file. The individual biological information is extracted from the soybean gene file and the value of the relationship between populations is computed. Then the degree of differentiation of the two populations of wild soybean and cultivated soybean is obtained. Firstly, the easy parallel serial algorithm is designed. Then according to the...
We propose an effective modified differential evolution algorithm (EMDE) to solve reliability problems in this paper. The proposed algorithm modifies the mutation operator and crossover operator of the original differential evolution algorithm (DE). The modified mutation operator enables all solution vectors to mimic the global best solution vector with linearly increased probability, and the modified...
The paper concerns the problem of the allocation of processes in mesh structured systems. The implemented optimization algorithm is based on the idea of Differential Evolution. The algorithm can be tuned along with ten different mutation schemes. The essential aim of the paper is checking these mutation schemes' impact on the efficiency of the considered algorithm. The studies are based on the simulations...
In this paper, improved differential evolution (IDE) algorithm with variable 'del' parameter is presented. Recently developed IDE algorithm is used for global optimization of non-linear complex problems. The 'del' parameter is a predetermined level of tolerance for predefined allowable number of iterations which triggers the different mutation schemes in restart mechanism phase of IDE algorithm. The...
Learning is the core of intelligence algorithm. Genetic algorithm (GA), as an intelligent algorithm, has its own learning mechanism. This paper focuses on the learning matrix of evolutionary operators in GA. From the viewpoint of solution generation, the learning mechanism in GA is studied and the matrix expression of recombination and mutation is given. A new insight of GA from learning viewpoint...
In several traditional algorithms for optimization, the complexity of algorithm increases with expansion of system infrastructure. Resource constraint, cost and energy availability of wireless sensor network (WSN) demand a better node localization algorithm that doesn't require extra hardware as well as has better accuracy with good convergence. Sensor node localization is the ability of an individual...
Genetic Algorithm is based on natural evolution. The genetic algorithm is a probabilistic search algorithm that iteratively transforms a set (called a population) of mathematical objects (typically fixed-length binary character strings), each with an associated fitness value, into a new population of offspring objects using the Darwinian principle of natural selection. The research in genetic algorithm...
In this paper, a novel improved multiobjective particle swarm optimization (IMOPSO) is proposed for solving the optimal reactive power dispatch (ORPD) problem with multiple and competing objectives. In order to improve the global search capability and the nondominated solutions diversity, time variant parameters, mutation operator, and dynamic crowding distance are incorporated into the MOPSO algorithm...
Differential evolution (DE) is a population-based optimizer that obtains the solution by iteratively improving the given measure of quality. In this work, DE has been applied in FIR Filter design to optimize the frequency response and the signed-power-of-two (SPT) terms of the filter coefficients. DE is influenced mainly by three design parameters, Strategy S, Mutant factor F and Cross-over probability...
In this paper, we investigate performances of Evolutionary Programming (EP) for optimization problems. An individual of EPs has searching optimum points and strategy parameters. The procedures of EPare consists of an initialization, a mutation, anevaluation of the individual and a selection. These processes are repeated while termination conditions are not satisfied. Experimental results show an efficiency...
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