Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Engineering Optimization Using a Simple Evolutionary Algorithm
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization
Journal of Global Optimization
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
IEEE Transactions on Evolutionary Computation
A Modified Particle Swarm Optimization for Practical Engineering Optimization
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 03
League Championship Algorithm: A New Algorithm for Numerical Function Optimization
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Recognizing Team Formations in Multiagent Systems: Applications in Robotic Soccer
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
Useful infeasible solutions in engineering optimization with evolutionary algorithms
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
Self-adaptive fitness formulation for constrained optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
DisABC: A new artificial bee colony algorithm for binary optimization
Applied Soft Computing
Investigating Multi-View Differential Evolution for solving constrained engineering design problems
Expert Systems with Applications: An International Journal
Constrained optimisation and robust function optimisation with EIWO
International Journal of Bio-Inspired Computation
A novel differential evolution algorithm for binary optimization
Computational Optimization and Applications
A particle swarm optimizer for grouping problems
Information Sciences: an International Journal
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The league championship algorithm (LCA) is a new algorithm originally proposed for unconstrained optimization which tries to metaphorically model a League championship environment wherein artificial teams play in an artificial league for several weeks (iterations). Given the league schedule, a number of individuals, as sport teams, play in pairs and their game outcome is determined given known the playing strength (fitness value) along with the team formation (solution). Modelling an artificial match analysis, each team devises the required changes in its formation (a new solution) for the next week contest and the championship goes for a number of seasons. In this paper, we adapt LCA for constrained optimization. In particular: (1) a feasibility criterion to bias the search toward feasible regions is included besides the objective value criterion; (2) generation of multiple offspring is allowed to increase the probability of an individual to generate a better solution; (3) a diversity mechanism is adopted, which allows infeasible solutions with a promising objective value precede the feasible solutions. Performance of LCA is compared with comparator algorithms on benchmark problems where the experimental results indicate that LCA is a very competitive algorithm. Performance of LCA is also evaluated on well-studied mechanical design problems and results are compared with the results of 21 constrained optimization algorithms. Computational results signify that with a smaller number of evaluations, LCA ensures finding the true optimum of these problems. These results encourage that further developments and applications of LCA would be worth investigating in the future studies.