OPTIMIZING VEHICLE ROUTING WITH A HYBRID SWARM-INTELLIGENT FROG JUMPING OPTIMIZATION ALGORITHM

Abstract

The issues in Vehicle Routing with Time Windows (VR-TW) are addressed in this study using a novel hybrid swarm-intelligent frog jumping optimisation (HSIFJO) algorithm. The method employs a diversity management strategy for developing memeplexes, which assists in preserving diversity and prevents the premature termination of the search. To increase population diversity and improve solution quality, an enhanced clone selection (CS) process is employed. To maximise the algorithm's potential, an enhanced and extended extremal optimisation (EO) strategy is used, coupled with different move operators. A proposed adaptive soft time windows (ASTW) surcharge approach acknowledges the possibility of impractical solutions during the evolution process. When compared to existing state-of-the-art heuristics, the suggested approach performs exceptionally well in performance evaluation.

Keywords

• vehicle routing problem
• hybrid swarm-intelligent frog jumping optimization (hsifjo)
• extremal optimization (eo)