Efficient Chaotic Imperialist Competitive Algorithm with Dropout Strategy for Global Optimization

Abstract

The imperialist competitive algorithm combined with chaos theory (CICA) demonstrates excellent performance in global optimization problems. However, its computational complexity increases with the introduction of chaotic maps. To address this, we integrate CICA with a dropout strategy that randomly samples the dimensions of each solution at each iteration of the computation. We investigate the potential of the proposed algorithm with different chaotic maps through six symmetric and six asymmetric benchmark functions. We also apply the proposed algorithm to AUVs’ path planning application showing its performance and effectiveness in solving real problems. The simulation results show that the proposed algorithm not only has low computational complexity but also enhances local search capability near the globally optimal solution with an insignificant loss in the success rate.

Evolutionary Algorithm to Optimize Process Parameters of Al/Steel Magnetic Pulse Welding

“…The colonies that have a θ degree of deviation and x units move towards the imperialists. This deviation leads to a more thorough exploration of the decision space, as shown in Figure 6 [31]. θ and x are uniformly distributed random numbers.…”

“…θ and x are uniformly distributed random numbers. β is a number greater than one, d represents the distance between the imperialists and colonies, and γ is a parameter that determines the extent of deviation from the original direction [28,[30][31][32].…”

“…where T.C n is the total cost of the nth empire, and ξ is a positive number less than one. Finally, the competition continues until there is only one imperialist, representing the strongest empire in the search space; this is known as empirical convergence [28][29][30][31][32].…”

“…Several evolutionary algorithms have been developed to address optimization problems across a range of scientific and engineering disciplines. These algorithms include the Genetic Algorithm (GA) [23][24][25][26][27], the ant colony optimization algorithm, the bee algorithm, and the Imperialist Competitive Algorithm (ICA) [28][29][30][31][32]. These algorithms establish robust process parameter combinations for achieving superior joint quality.…”

Color Revolution: A Novel Operator for Imperialist Competitive Algorithm in Solving Cloud Computing Service Composition Problem

“…In addition, service time and service cost have been identified as the two most important QoS parameters in cloud service composition that should be further considered in optimizing the QoS [4]. Hence, in this paper, the researchers are strongly motivated to focus on service time and cost optimization for CCSC by improving the imperialist competitive algorithm (ICA) [18][19][20].…”