Optimizing the Capacitated Electric Vehicle Routing Problem using Chaotic Discrete Chimp Optimization Algorithm

Abstract

This work addresses the capacitated electric vehicle routing problem (EVRP) integrating vehicle capacity limits with battery and charging constraints. We propose the Chaotic Discrete Chimp Optimization Algorithm (CDChOA), which combines coordinated hunting behavior with the Tinkerbell chaos map to balance exploration and exploitation. EV routes with optional charging visits are encoded as discrete solutions with problem-specific operators enforcing capacity, battery consumption, and charging feasibility. Evaluated on standard EVRP benchmarks against an adaptive NEH-NN heuristic and Genetic Algorithm, CDChOA achieves competitive or superior solution quality in total routing cost, particularly on large-scale instances with complex charging requirements. Statistical analysis confirms CDChOA often outperforms GA on large instances, supporting its potential for sustainable freight planning.