The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability, it is still worth paying attention to problems of falling into local optima and lower convergence accuracy. This paper proposes some improvements to the salp swarm algorithm that are based on a nonlinear dynamic weight and the mapping mutation operation. Firstly, the nonlinear dynamic weight is helpful for further optimizing the transition from exploration to exploitation and alleviating the local optima stagnation phenomena. Secondly, utilizing a mapping mutation operation can increase the diversity of followers in algorithm, to avoid getting trapped into the local optima during the search and provide a better optimal solution. The proposed algorithm is characterized by a stronger global optimization capability and high convergence accuracy. Finally, to confirm the effectiveness of the proposed algorithm, comparative experiments based on other well-known swarm-based algorithms and each improvement for the original algorithm are conducted. The quantitative results and convergence curves among several algorithms demonstrate that the enhanced algorithm with the nonlinear dynamic weight and mapping mutation operation can outperform the original algorithm.

In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population , and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.

The original Honey Badger Algorithm as one of the newest meta¬heuristic techniques has a better convergence speed. However, HBA has the potential disadvantages of poor convergence accuracy, insufficient balancing among exploration and exploitation, and the propensity to slip into local optimization. In this paper, a novel golden sinusoidal survival honey badger algorithm is proposed. Firstly, an opposing learning and chaos mechanism are applied to the initial individual generation so that they can be distributed throughout the entire search area, which improves the precision of initial populations. Secondly, in the position update phase, we use a nonlinear convergence strategy to balance the weight of prey in the next walk and to increase the global search ability. After that, evaluating the quality of honey badger by golden sinusoidal survival rate and updating precocious individuals by Lévy flight, through which the premature convergence of the algorithm can be avoided. Finally, 23 benchmark function, CEC2019 tests are employed to assess the effectivity of improved algorithm. Test results indicate that the algorithm's capabilities to evolve, to extricate the local optimal and to detect the global optimal placements are enhanced.

In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population, and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.

The salp swarm algorithm imitates the swarm behavior of salps during navigation and hunting that has been proven the superiority of search for best solution. However, although it has sufficient global search ability , it’s still worth paying attention to problems of falling into local optima and lower convergence accuracy. This paper proposes some improvements to salp swarm algorithm that are based on a nonlinear dynamic weight and the mapping mutation operation. Firstly, the non-linear dynamic weight is helpful for further optimizing the transition from exploration to exploitation and alleviating the local optima stagnation phenomena. Secondly, utilizing a mapping mutation operation can increase the diversity of followers in algorithm, on behalf of avoid getting trapped into the local optima during the search and provide a better optimal solution. The proposed algorithm is characterized by stronger global optimization capability and high convergence accuracy. Finally, to confirm the effectiveness of proposed algorithm, comparative experiments based on other well-known swarm-based algorithms and each improvement for the original algorithm are conducted. The quantitative results and convergence curves among several algorithms demonstrate that the enhanced algorithm with the nonlinear dynamic weight and mapping mutation operation can outperform the original algorithm.