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

Harris Hawks Optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by Hénon Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.