Differential Search Algorithm: A modernized particle swarm optimization algorithm

Differential Search Algorithm (DSA) is a new and effective evolutionary algorithm for solving real-valued numerical optimization problems. DSA was inspired by migration of superorganisms utilizing the concept of stable-motion. In [1], the problem solving success of DSA was compared to the successes of ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011 and CMA-ES algorithms for solution of numerical optimization problems.

DSA is a multi-strategy based, advanced evolutionary algorithm. DSA analogically simulates a superorganism that migrates between two stopovers. Standard DSA has four different search-methods; bijective-DSA (B-DSA), surjective-DSA (S-DSA), Elitist(1)-DSA (E1-DSA), and Elitist(2)-DSA (E2-DSA). Hybridization of DSA (H-DSA) search methods is quite easy.

See www.pinarcivicioglu.com/ds.html for detailed information and updated-versions of DSA.

Related references;

1. P. Civicioglu, "Transforming Geocentric Cartesian Coordinates to Geodetic Coordinates by Using Differential Search Algorithm", Computers and Geosciences, 46, 229-247, 2012.

2. P. Civicioglu, "Backtracking Search Optimization Algorithm for numerical optimization problems", Applied Mathematics and Computation, 219, 8121–8144, 2013.

3. P. Civicioglu, "Artificial cooperative search algorithm for numerical optimization problems",Information Sciences, 229, 58–76, 2013.

4. P. Civicioglu, E. Besdok, "A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms", Artificial Intelligence Review, 39 (4), 315-346, 2013.