The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Particle swarm approach for structural design optimization
Computers and Structures
Journal of Global Optimization
A simulated annealing algorithm for manufacturing cell formation problems
Expert Systems with Applications: An International Journal
An ACO algorithm to design UMTS access network using divided and conquer technique
Engineering Applications of Artificial Intelligence
Data aggregation in wireless sensor networks using ant colony algorithm
Journal of Network and Computer Applications
Information Sciences: an International Journal
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
Comments on "A note on teaching-learning-based optimization algorithm"
Information Sciences: an International Journal
International Journal of Metaheuristics
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This work presents a new optimization technique called Grenade Explosion Method (GEM). The fundamental concepts and ideas which underlie the method are fully explained. It is seen that this simple and robust algorithm is quite powerful in finding all global and some local optima of multimodal functions. The method is tested with several multimodal benchmark functions and the results show it usually converges to the global minima faster than other evolutionary methods such as Genetic Algorithm (GA) and Artificial Bee Colony (ABC). Based on the performance on classical benchmark functions, the efficiency of the method in solving engineering applications can be highly appreciated.