JADE Optimization Algorithm Based on Differential Evolution

Differential Evolution (DE) has gained significant attention as a simple yet efficient evolutionary computation method in the optimization field in recent years. Among various improved versions, the JADE algorithm stands out as a classic representative due to its innovative adaptive mechanism.

The most notable enhancement in JADE lies in its parameter adaptation capability. Traditional DE algorithms require manual configuration of parameters such as mutation factor and crossover probability, whereas JADE implements a historical memory mechanism that automatically adjusts these critical parameters based on performance during the search process. This adaptive characteristic enables consistent performance across different problems and significantly reduces parameter tuning efforts. In code implementation, this typically involves maintaining success-history archives and using arithmetic means or Lehmer means to generate new parameter values.

The algorithm's core innovation consists of two key mechanisms: first, an archive-based external population that stores historically unsuccessful mutation vectors, providing additional information for subsequent searches; second, a parameter adaptation strategy that guides new parameter generation by maintaining historical records of successful parameter combinations. This design enables JADE to balance exploration capability and exploitation efficiency effectively. From a programming perspective, this requires implementing archive management routines and parameter adaptation functions that update based on successful trial vectors.

JADE demonstrates clear advantages when solving complex optimization problems, particularly for nonlinear, multimodal challenges. Its adaptive nature allows automatic balancing between global exploration and local exploitation, effectively preventing premature convergence. These characteristics make JADE a valuable tool in engineering optimization and machine learning parameter tuning applications, where its self-adapting nature reduces implementation complexity while maintaining solution quality. The algorithm can be implemented using population initialization, mutation operations with archive integration, crossover mechanisms, and selection procedures with parameter adaptation updates.