Abstract
Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimization algorithm(IGWO) is proposed. The lion optimizer algorithm and dynamic weights are integrated into the original grey wolf optimization algorithm. When the positions of $\alpha $ wolf, $\beta $ wolf, and $\delta $ wolf are updated, the lion optimizer algorithm is used to add disturbance factors to the wolves to give $\alpha $ wolf, $\beta $ wolf, and $\delta $ wolf active search capabilities. Dynamic weights are added to the grey wolf position update to prevent wolves from losing diversity and falling into local optimum. Through multiple benchmark function test experiments and path planning experiments, the experimental results show that the improved grey wolf optimization algorithm can effectively improve the accuracy and convergence speed, and the optimization effect is better.
Highlights
Robot Path Planning (RPP) is one of the important topics in the field of robotics research
Grey Wolf Optimization (GWO) algorithm is a new swarm intelligence algorithm inspired by the grey wolf leadership hierarchy and group hunting behavior in nature by Mirjalili et al.[10].Due to its simple principle, few parameters, easy programming, and support for distributed parallel computing and strong global search capabilities, the GWO algorithm is widely used in global optimization problems in the fields of computer science[11], engineering science[12], and management science[13]
This paper proposes a new and improved grey wolf algorithm, which combines the lion optimizer algorithm and adds dynamic weights to increase the diversity of the population and enhance the search ability of the population, so that the grey wolf algorithm further improves the convergence speed and convergence accuracy
Summary
Robot Path Planning (RPP) is one of the important topics in the field of robotics research. With the development of swarm intelligence algorithms, new ideas have been provided for RPP problems. Many swarm intelligence optimization algorithms have been proposed and widely used in research fields and practical scenarios, such as Particle Swarm Optimization (PSO)[6], Ant Colony Optimization (ACO)[7], Artificial Bee Colony algorithm (ABC)[8] and Harris Hawks Optimization (HHO)[9], etc. Grey Wolf Optimization (GWO) algorithm is a new swarm intelligence algorithm inspired by the grey wolf leadership hierarchy and group hunting behavior in nature by Mirjalili et al.[10].Due to its simple principle, few parameters, easy programming, and support for distributed parallel computing and strong global search capabilities, the GWO algorithm is widely used in global optimization problems in the fields of computer science[11], engineering science[12], and management science[13].
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