An improved Escape algorithm with multi-strategy enhancement for solving global optimum problems

The Escape algorithm (ESC), which simulates crowd evacuation behavior, is a novel algorithm. However, it has certain limitations in terms of convergence speed and the avoidance of local optima. This paper proposes a multi-strategy enhanced variant (ESCSA) which introduces three key improvements: First, it incorporates a dynamic mask probability adjustment mechanism that adapts the dimension update ratio throughout the iteration process, thereby achieving a better balance between exploration and exploitation. Second, it incorporates stagnation detection and Simulated Annealing local search. When the optimal solution shows no significant improvement over several consecutive iterations, the Simulated Annealing optimization mechanism is triggered to generate Gaussian-perturbed neighboring solutions, avoiding local optima via the Metropolis criterion. Third, it implements a worst-elimination strategy, periodically injecting randomly positioned new individuals to replace the worst solutions in the population, thereby maintaining diversity. On the CEC 2017 benchmark suite (10/30/50/100 dimensions), ESCSA achieved the best overall performance among 11 algorithms, ranking first in the Friedman mean rank test across all four dimensions with average ranks of 2.217, 1.383, 1.400, and 1.933, respectively. It obtained the optimal mean fitness on 17, 22, 21, and 17 functions for the four dimensions, respectively. In three engineering design problems (speed reducer design, multi-disc clutch braking, and rolling element bearing design), ESCSA ranked second, third, and first, respectively, demonstrating strong and consistent performance across constrained optimization tasks. When subsequently applied to path planning for mobile robots in static environments, ESCSA was experimentally confirmed to plan robot paths and identify superior solutions efficiently.