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BPSOGSA gravity and the search algorithm based on
4.0
BPSOGSA as a binary version of the mixed PSOGSA, and is used to solve the problem of the binary optimizations. The algorithm takes into account the integration of Adaptive value to further balance BPSOGSA local and global search capabilities.
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BGSA binary gravitational force search algorithms
1.0
GSA works through the particle will be searched as a group of objects running in space, attracts bodies through gravitational interactions between objects run follows the laws of dynamics. Moderately larger its inertial mass of the particle, the greater the value, so that gravity will push towards a massive object to move the object, thus gradually approaching finds the optimal solution of optimization problems. GSA has a strong capability of global search and speed. As the GSA research progresses, it is more and more widely used, and gradually caught the attention of scholars. Applying this algorithm to binary thought to gravity in search algorithms, and expanding the scope of its application!
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1
Ant Colony algorithm Matlab code
4.0
Ant Colony algorithm was originally proposed by Dorigo [3], is a new heuristic approach for solving combinatorial optimization problems. This method is characterized by positive feedback, distributed computing, as well as constructive greedy heuristic search. Positive feedback helps you quickly find a better solution; Distributed computing to avoid the appearance of prematurity in the iterative process; Greedy heuristic search use the search process as early as possible to find acceptable solutions. Although the ACO appeared only in recent years, but has been successfully applied to many combinatorial optimization problems, such as TSP, JSP and so on.
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Ant Colony algorithm MATLAB source code
4.0
ACO (Ant Colony algorithm,ACA) largely mimic the mechanism of Ant foraging through a number of artificial ants communicate with each other, collaborate, quickly find the optimal path. ACA is a generic new heuristic method for solving combinatorial optimization problems, which multiple agents (artificial ants) information exchange between completing the complex behavior of the colony, and accelerated through the positive feedback optimal solution search process uses distributed computing to avoid getting trapped into local
The optimal solution.
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