An evaluating of the Sniffer global optimization algorithm using standard test functions
Journal of Computational Physics
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An introduction to genetic algorithms
An introduction to genetic algorithms
Enhanced simulated annealing for globally minimizing functions of many-continuous variables
ACM Transactions on Mathematical Software (TOMS)
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
A novel metaheuristics approach for continuous global optimization
Journal of Global Optimization
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.01 |
In this paper we develop and test a novel stochastic algorithm SNE (Shrinking Neighborhood Evolution) based on the issue of bound constrained optimization problem. Its heuristic strategy is simple and direct-related to the search region of the solving problem based on the concept of "k-box-neighborhood" -defined in this paper. Our numerical experiments show that the optimization capability of SNE is competing to other congeneric algorithms such as Particle Swarm Optimizer (PSO), Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) and Differential Evolution (DE). The new method requires few control parameters, easy to use, and has promising potentials to parallel computation.