Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ant Colony Optimization
Constrained optimization via particle evolutionary swarm optimization algorithm (PESO)
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A review of adaptive population sizing schemes in genetic algorithms
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Two hybrid differential evolution algorithms for engineering design optimization
Applied Soft Computing
Grenade Explosion Method-A novel tool for optimization of multimodal functions
Applied Soft Computing
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
Information Sciences: an International Journal
Unified particle swarm optimization for solving constrained engineering optimization problems
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Useful infeasible solutions in engineering optimization with evolutionary algorithms
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Engineering Applications of Artificial Intelligence
Comments on "A note on teaching-learning-based optimization algorithm"
Information Sciences: an International Journal
An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems
Journal of Intelligent Manufacturing
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This paper presents the performance of an elitist teaching-learning-based optimisation algorithm on a class of constrained design optimisation problems. Teaching-learning-based optimisation TLBO is a recently proposed population-based algorithm which simulates the teaching-learning process of the class room. This algorithm requires only the common control parameters and does not require any algorithm-specific control parameters. The effect of elitism on the performance of the TLBO algorithm is investigated in this paper while solving the constrained benchmark problems. The effects of common control parameters such as the population size and the number of generations on the performance of the algorithm are also investigated. Twenty-one benchmark problems taken from the literature related to constrained design optimisation are used to test the elitist TLBO performance. Experimental results show that the elitist TLBO is superior or competitive to other optimisation algorithms for the problems considered.