Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Ant algorithms for discrete optimization
Artificial Life
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The distributed multilevel ant-stigmergy algorithm used at the electric-motor design
Engineering Applications of Artificial Intelligence
Parameter-less evolutionary search
Proceedings of the 10th annual conference on Genetic and evolutionary computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic algorithm for test pattern generator design
Applied Intelligence
Production scheduling with a memetic algorithm
International Journal of Innovative Computing and Applications
MatPort – online mathematics learning with a bioinspired decision-making system
International Journal of Innovative Computing and Applications
Differential evolution for parameterized procedural woody plant models reconstruction
Applied Soft Computing
Guided restarting local search for production planning
Engineering Applications of Artificial Intelligence
SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Parameter-less algorithm for evolutionary-based optimization
Computational Optimization and Applications
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The efficiency of universal electric motors that are widely used in home appliances can be improved by optimizing the geometry of the rotor and the stator. Expert designers traditionally approach this task by iteratively evaluating candidate designs and improving them according to their experience. However, the existence of reliable numerical simulators and powerful stochastic optimization techniques make it possible to automate the design procedure. We present a comparative study of six stochastic optimization algorithms in designing optimal rotor and stator geometries of a universal electric motor where the primary objective is to minimize the motor power losses. We compare three methods from the domain of evolutionary computation, generational evolutionary algorithm, steady-state evolutionary algorithm and differential evolution, two particle-based methods, particle-swarm optimization and electromagnetism-like algorithm, and a recently proposed multilevel ant stigmergy algorithm. By comparing their performance, the most efficient method for solving the problem is identified and an explanation of its success is offered.