Aiming at the disadvantages of the standard seagull algorithm (SOA), such as discrete distribution of initial population position, low solution accuracy and slow convergence speed, a seagull optimization algorithm (L-SOA) which based on good point set mapping and integrating Levy flight and adaptive walking strategies is proposed. Uses good point set mapping to produce a better initial solution, select Levy flight and adaptive walk strategies for optimization by random probability to enhances the ability to jump out of local extreme and improves the convergence performance. Finally, compared with other four swarm intelligence algorithms on eight standard test functions, the results show that L-SOA algorithm has higher precision, faster convergence speed and more stable robustness.
Aiming at the problems that sparrow search algorithm is easy to fall into local optimum, slow convergence speed and insufficient convergence accuracy, an Improved Sparrow Search Algorithm (ISSA) with weighted mutation and neighborhood perturbation is proposed. Improve the follower position update formula and add the weighted mutation differential evolution algorithm to maintain the population diversity and balance the global exploration and local development ability of the algorithm. Combining attack with reverse search and variable spiral position update, the algorithm perturbs the individual position around the optimal neighborhood, jumps out of the local optimization, and improves the convergence speed and accuracy of the algorithm. The performance of the improved algorithm is tested on 12 benchmark functions. The experimental results show that the improved algorithm has better optimization accuracy, convergence performance and stability.
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