2007 · 2.6K citations · 10 references
ComputingComputer ScienceAlgorithmsArtificial IntelligenceOptimizationCombinatorial OptimizationEvolutionary ComputationEvolutionary OptimizationMetaheuristic OptimizationOptimization TheoryPopulation-based SearchEvolutionary AlgorithmsEvolutionary SearchPopulation-based Optimization
Evolutionary algorithms typically begin with an initial population of candidate solutions. This paper introduces an optimization algorithm inspired by imperialistic competition. The algorithm represents solutions as countries, forming empires of imperialists and colonies, and iteratively lets weaker empires collapse while stronger ones absorb colonies until a single empire remains. Experiments on benchmark cost functions demonstrate the algorithm’s effectiveness across various optimization problems.
This paper proposes an algorithm for optimization inspired by the imperialistic competition. Like other evolutionary ones, the proposed algorithm starts with an initial population. Population individuals called country are in two types: colonies and imperialists that all together form some empires. Imperialistic competition among these empires forms the basis of the proposed evolutionary algorithm. During this competition, weak empires collapse and powerful ones take possession of their colonies. Imperialistic competition hopefully converges to a state in which there exist only one empire and its colonies are in the same position and have the same cost as the imperialist. Applying the proposed algorithm to some of benchmark cost functions, shows its ability in dealing with different types of optimization problems.
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Computing, Mathematics, Algorithms +12