A mixture of Bisphenol-A analogues elicits neuro-hepatic dysfunction in female rat model

Abstract

Optimization is a central challenge in scientific discovery, engineering design, and decision-making. While population-based metaheuristics such as Genetic Algorithms and Particle Swarm Optimization have received significant attention, Trajectory-based algorithms remain relatively underrepresented despite their simplicity and efficiency. Trajectory algorithms operate by iteratively refining a single solution, tracing a path through the search space, and balancing exploration with exploitation through localized decision rules. This review study revisits four classical trajectory algorithms; Simulated Annealing (SA), Hill Climbing (HC), Tabu Search (TS), and the Great Deluge Algorithm (GDA), and provides a comparative review of their mechanisms, strengths, and limitations. Simulated Annealing balances exploration and exploitation through a temperature-controlled acceptance rule, while Hill Climbing represents a purely greedy search that quickly converges to local optima. Tabu Search improves upon this by using adaptive memory structures to avoid cycling and  diversify the search, and the Great Deluge Algorithm employs a deterministic threshold (water level) that steadily rises to control solution acceptance. Each algorithm is analyzed with reference to its pseudocode, showing how exploration and exploitation are achieved in practice. The review highlights why these algorithms, once overshadowed by population-based methods, are gaining renewed attention. Their low memory footprint, computational efficiency, and ease of hybridization make them suitable for modern contexts.