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 brownian like 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.

Standard-Code of DSA in Matlab

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, "Circular Antenna Array Design by Using Evolutionary Search Algorithms", Progress In Electromagnetics Research B, Vol. 54, 265-284, 2013.
3. Waghole, V., Tiwari, R., "Optimization of needle roller bearing design using novel hybrid methods",Mechanism and Machine Theory, 72,71-85, 2014.
4. P. Civicioglu, "Backtracking Search Optimization Algorithm for numerical optimization problems", Applied Mathematics and Computation, 219, 81218144, 2013.
    See http://www.pinarcivicioglu.com/bsa.html  for source code of BSA.
5. P. Civicioglu, "Artificial cooperative search algorithm for numerical optimization problems",Information Sciences, 229, 5876, 2013.
6. 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.

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for updated versions of DSA code.
Differential Search Algorithm (DSA)
for Numerical Optimization Problems

Pinar Civicioglu