APEX-DE: Adaptive Parameter Control and Selection Strategy for Differential Evolution With Exponential Crossover

Since the proposal of the differential evolution (DE) algorithm, most improvement efforts have concentrated on binomial crossover. However, we find that DE variants using exponential crossover can also outperform those using binomial crossover in optimization performance, provided that an appropriate parameter control scheme is employed. To develop a high-performance DE algorithm with exponential crossover, this article introduces an adaptive parameter control and selection strategy for DE with exponential crossover (APEX-DE). The main contributions of APEX-DE are summarized as follows: First, a novel adaptive parameter control (APC) technique is proposed, incorporating an automatically generated crossover rate $CR$ , a dual-stage scale factor $F$ generation mechanism, and a new adaptive strategy for the scale parameter $\sigma _{F}$ . Second, a novel selection mechanism is introduced to replace the classical DE selection, thereby enhancing the ability to escape from local optima. Third, a redirection strategy is developed to regenerate individuals and adjust their scale factor $F$ , enhancing evolutionary potential. APEX-DE is evaluated on a large test suite comprising 88 benchmark functions, as well as on a challenging uncrewed aerial vehicle (UAV) path-planning task in multithreat environments. Experimental evaluation confirms that APEX-DE delivers better performance than a broad set of state-of-the-art algorithms.