Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Theoretical Computer Science - Natural computing
A Hybrid Metaheuristic for the Quadratic Assignment Problem
Computational Optimization and Applications
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Hybrid Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Expert Systems with Applications: An International Journal
Synergy of evolutionary algorithm and socio-political process for global optimization
Expert Systems with Applications: An International Journal
An improved GA and a novel PSO-GA-based hybrid algorithm
Information Processing Letters
Imperialist Competitive Algorithm Using Chaos Theory for Optimization (CICA)
UKSIM '10 Proceedings of the 2010 12th International Conference on Computer Modelling and Simulation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
This paper proposes a new approach by combining the Evolutionary Algorithm (EA) and socio-political process based Imperialist Competitive Algorithm (ICA). This approach tries to capture several people involved in community development characteristic. People live in different type of communities: Monarchy, Republic, Autocracy and Multinational. Leadership styles are different in each community. Research work has been undertaken to deal with curse of dimensionality and to improve the convergence speed and accuracy of the basic ICA and EA algorithms. The proposed algorithm has been compared with some well-known heuristic search algorithms. The obtained results confirm the high performance of the proposed algorithm in solving various benchmark functions specially in high dimensional problem. Simulation results were reported and the SBA indeed has established superiority over the basic algorithms with respect to set of functions considered and it can be employed to solve other global optimization problems, easily. The results show the efficiency and capabilities of the new hybrid algorithm in finding the optimum. Amazingly, its performance is about 85% better than other algorithms such as EA and ICA. The performance achieved is quite satisfactory and promising for all test functions.