Seagull algorithm based on good point set and dual hybrid strategy
Dianxing LIU, Shuiping Zhang, Wang Bi, Zuopeng LI · 2023
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.