Search-Trajectory Patterns as Feedback for Adaptive Metaheuristic Configuration Control

Source abstract

Adaptive decisions are often based on algorithm-specific feedback or simple indicators that provide limited information about the evolving search dynamics. This work investigates explicit pattern-based analysis of the search trajectory as a source of feedback for adaptive configuration control. A trajectory-driven framework is proposed in which Derivative Dynamic Time Warping (DDTW) analyzes the recent best-so-far fitness trajectory against reference patterns representing sustained progress and lack of progress. The resulting information regulates switching between predefined exploration- and exploitation-oriented parameter configurations. Two control strategies are introduced: Binary-Simple, which reacts directly to trajectory evidence associated with a plateau, and Binary-Complex, which incorporates multiple search conditions, temporal persistence, and asymmetric switching. The framework is evaluated on 27 Multidimensional Knapsack Problem instances using BPSO, GA, BGWO, and BDE, with 31 independent runs per condition. Results show that no fixed configuration consistently dominates across metaheuristics and instances, while the effects of trajectory-driven adaptation depend on the underlying algorithm. Across 108 metaheuristic–instance conditions, Binary-Complex achieves the best mean rank (2.13). The Friedman test detects an overall difference among strategies (p=7.73×10−5); Bonferroni-Dunn post-hoc comparisons favor Binary-Complex over both fixed configurations, but do not establish a difference from Binary-Simple. Moreover, a common trajectory-analysis parameterization produces different switching behaviors across the evaluated metaheuristics. These findings support explicit search-trajectory pattern analysis as a viable feedback mechanism for adaptive configuration control across different population-based metaheuristics.