The Wading Across Stream Algorithm

Abstract According to the idea of wading across the stream by feeling the way, a kind of fast efficient random optimization algorithm is proposed. The wading across stream algorithm (WSA) acts as a solution as a start point, then searches several random solutions near the start point, and finds the best of these solutions. This best solution is taken as the next start point, and then several random solutions near this start point are searched, and so on. For solving continuous optimization problem, the improved wading across stream algorithm (IWSA) gradually shrinks the search space. The experimental results of some classic benchmark functions show that the proposed optimization algorithms improve extraordinarily the convergence velocity and precision. For solving the travelling salesman problem, the improved method selected the best of the initial solution as the start solution. To search the neighbourhood trial solution, four strategies are proposed. It is proved that reversal strategy is a simple and effective algorithm.

[1]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[2]  Tian Dongpin Research of chaos particle swarm optimization algorithm , 2013 .

[3]  Liu Wen An Improved Ant Colony Algorithm for Continuous Domain and Its Convergence Analysis , 2013 .

[4]  Gao Guohua USING SIMULATED ANNEALING ALGORITHM WITH SEARCH SPACE SHARPENING TO SOLVE TRAVELING SALESMAN PROBLEM , 1999 .

[5]  R. Saranya,et al.  Hybrid Artificial Bee Colony Algorithm and Simulated Annealing Algorithm for Combined Economic and Emission Dispatch Including Valve Point Effect , 2013, SEMCCO.

[6]  Rajeeva Kumar,et al.  Analysis and parameter selection for an Adaptive Random Search algorithm , 2005, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[7]  Carlos Henggeler Antunes,et al.  Comparison of a genetic algorithm and simulated annealing for automatic neural network ensemble development , 2013, Neurocomputing.

[8]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[9]  Dennis Guster,et al.  On parallel implementation of a discrete optimization random search algorithm , 2012, ArXiv.

[10]  Lev Kazakovtsev,et al.  Random Search Algorithm for the p-Median Problem , 2013, Informatica.

[11]  Cungen Cao,et al.  A Novel Ant Colony Genetic Hybrid Algorithm , 2010, J. Softw..

[12]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[13]  Gao Shang,et al.  Research on Chaos Particle Swarm Optimization Algorithm , 2006 .

[14]  S. Khatun,et al.  A Random search based effective algorithm for pairwise test data generation , 2011, International Conference on Electrical, Control and Computer Engineering 2011 (InECCE).

[15]  Shui Lin Tu,et al.  The Adaptive Stochastic Resonance Signal Detection System Based on the Multi-Point Random Search Algorithm , 2013 .

[16]  Ling Qiu,et al.  Estimation of Distribution Algorithms for Knapsack Problem , 2014, J. Softw..

[17]  Juan Julián Merelo Guervós,et al.  Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal , 2013, Soft Comput..