Enhancing Genetic Algorithm with PSO Particle Swarm Optimization as an Additive Operator
Utilizing PSO (Particle Swarm Optimization) as an additive operator to improve GA (Genetic Algorithm) performance through hybrid optimization strategy.
MATLAB Implementation of Enhanced Genetic Algorithm with Natural Number Encoding
This program implements an improved genetic algorithm utilizing a novel natural number encoding scheme, which significantly reduces chromosome length and enhances computational efficiency. The algorithm replaces traditional binary encoding with natural number representation for more intuitive gene expression.
MATLAB Implementation of an Enhanced Genetic Algorithm
Enhanced genetic algorithm program for optimal parameter selection with improved performance and practical usability
MATLAB Implementation of Improved Genetic Algorithm with Code Enhancements
Enhanced genetic algorithm MATLAB source code containing multiple subfiles. Execute the mainpath.m file for testing the complete algorithm implementation.
MATLAB Implementation of Improved Genetic Algorithm
An enhanced genetic algorithm program implemented in MATLAB, providing practical optimization capabilities with customizable features for various applications.
Multicast Routing Calculation Under Multiple QoS Constraints in Communication Networks
This paper presents an enhanced genetic algorithm named HGA-QoSR for multicast routing computation under multiple QoS constraints in communication networks. By integrating simulated annealing's local optimization capability with genetic algorithm's global search ability, and employing an isolated niche mechanism to control population evolution, the algorithm maintains ecological diversity during evolution. This approach improves computational efficiency and solution quality. Theoretical analysis and simulation experiments demonstrate significant performance improvements compared to traditional genetic algorithms, with implementation featuring adaptive mutation rates and fitness-based selection mechanisms.
MATLAB Implementation of Enhanced Genetic Algorithm for Passive Filter Optimization
Utilizing an enhanced genetic algorithm to optimize passive filter parameters considering three key aspects: cost minimization, reactive power compensation effectiveness, and filtering performance
ADPTIVE GA: An Enhanced Genetic Algorithm Implementation
ADPTIVE GA is an improved genetic algorithm program featuring diverse crossover operators and mutation operators, delivering robust computational capabilities for complex optimization problems.
Optimizing Fuzzy Control Membership Functions Using an Enhanced Genetic Algorithm
Utilizing an enhanced genetic algorithm to optimize fuzzy control membership functions, enabling automated parameter tuning for membership degrees through evolutionary computation techniques.