Dynamic Leader Selection and Gradient Climbing Techniques in Swarm Optimization

An important challenge of swarm robotics in practical applications lies with optimizing swarm navigation especially in dynamic or time-changing environments, which may affect the swarm’s overall performance. One technique to guide swarm behavior is by the use of the gradient climbing algorithm. This is an optimization technique where agents move towards increasing values of a scalar field, such as heat intensity or gas concentration, based on local gradient information and enables agents to navigate towards areas of interest by iteratively adjusting their positions to maximize the gradient. In complex and dynamic environments, achieving optimality may be difficult if appropriate swarm leadership strategies are absent. Leader selection entails identifying certain agents that may possess superior sensing capabilities, computational power, or strategic positioning within the swarm to guide the swarm behavior and decision-making. Therefore, this study develops an algorithm for dynamic leadership selection in swarm robotics for operations in changing environments such as in forest fires. Using the gradient information, leadership roles are assigned within the swarm to robots with the highest gradient value, which allows the algorithm to adapt to changing environmental conditions and improves the overall navigation towards the desired gradient maxima. The convergence of the swarm to the global maxima is evaluated through simulations, and shows that swarms with dynamic leader selection have convergence times that are less than half of the convergence times obtained in swarms with fixed leaders and swarms with no leaders selected. Also, the algorithm results in a reduced exploration area corresponding to improved energy efficiency when compared to the swarm with fixed leaders. The results demonstrate the effectiveness of dynamic leader selection in optimizing swarm behaviour in changing environments and its potential for real-world applications.