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. 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.

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├── ds_code (optimized)
│   ├── circlefit.m
│   ├── ds.m
│   ├── main.m
│   ├── plotcircle.m
│   ├── rosenbrock.m
│   ├── speedreducer.m
│   ├── step2.m
│   └── weierstrass.m
├── ds_code (standard)
│   ├── circlefit.m
│   ├── ds.m
│   ├── main.m
│   ├── plotcircle.m
│   ├── rosenbrock.m
│   ├── speedreducer.m
│   ├── step2.m
│   └── weierstrass.m
└── 好例子网_DSA_matlab.zip

2 directories, 17 files