Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization

To overcome the limitations of Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known Multi-Strategy Improved (IFSA). The IFSA utilizes cube chaotic mapping strategy generate initial populations, which enhances quality set. Moreover, information feedback model is dynamically adjust based current fitness value, exchange between populations search capability itself. In addition, introduce Random Opposition Learning Elite Position Greedy Selection strategies constantly retain superior individuals while also reducing probability falling into optimum, thereby further enhancing convergence algorithm. We evaluate performance using 23 benchmark functions verify its optimization Wilcoxon rank-sum test. compared experiment results indicate that proposed can obtain higher accuracy better exploration abilities. It provides new for solving complex problems.