Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2015, Vol 6, Issue 12
Abstract
Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a self-adaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA’s competition at the Congress of Evolutionary Computing of 2009 (CEC’09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator.
Authors and Affiliations
Wali Mashwani, Abdellah Salhi, Muhammad jan, Rashida Khanum
Protection of Ultrasound Image Sequence: Employing Motion Vector Reversible Watermarking
In healthcare information systems, medical data is very important for diagnosis. Most of the health institutions store their patients’ data on third-party servers. Therefore, its security is very important, since the adv...
Depth Limitation and Splitting Criteria Optimization on Random Forest for Efficient Human Activity Classification
Random Forest (RF) is known as one of the best classifiers in many fields. They are parallelizable, fast to train and to predict, robust to outlier, handle unbalanced data, have low bias, and moderate variance. Apart fro...
Automatic Sign Language Recognition: Performance Comparison of Word based Approach with Spelling based Approach
Evolution of computer based interaction has been through a number of phases. From command line interface to menu driven environment to Graphics User Interface, the communication has evolved to a better user friendly envi...
Financial Literacy of SME Managers’ on Access to Finance and Performance: The Mediating Role of Financial Service Utilization
Considering financial literacy as a central factor for consumer demand for financial services, we analyze its impact on access and actual use of financial services and its ultimate consequential reflections on SMEs perfo...
A Parallel Simulated Annealing Algorithm for Weapon-Target Assignment Problem
Weapon-target assignment (WTA) is a combinatorial optimization problem and is known to be NP-complete. The WTA aims to best assignment of weapons to targets to minimize the total expected value of the surviving targets....