Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Adaptive global optimization with local search
Adaptive global optimization with local search
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Stochastic Global Optimization: Problem Classes and Solution Techniques
Journal of Global Optimization
Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Differential evolution algorithms using hybrid mutation
Computational Optimization and Applications
An electromagnetic meta-heuristic for the nurse scheduling problem
Journal of Heuristics
Solving the sum-of-ratios problem by a stochastic search algorithm
Journal of Global Optimization
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Modified movement force vector in an electromagnetism-like mechanism for global optimization
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
Expansive competitive learning for kernel vector quantization
Pattern Recognition Letters
Journal of Global Optimization
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
A Hybrid Electromagnetism-Like Algorithm for Single Machine Scheduling Problem
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
A global optimization method for solving fuzzy relation equations
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Fractional-order PID controller optimization via improved electromagnetism-like algorithm
Expert Systems with Applications: An International Journal
An electromagnetism-like method for nonlinearly constrained global optimization
Computers & Mathematics with Applications
A new electromagnetism-like algorithm with a population shrinking strategy
ICOSSSE'07 Proceedings of the 6th WSEAS international conference on System science and simulation in engineering
A new electromagnetism-like algorithm with a population shrinking strategy
MACMESE'07 Proceedings of the 9th WSEAS international conference on Mathematical and computational methods in science and engineering
An augmented Lagrangian fish swarm based method for global optimization
Journal of Computational and Applied Mathematics
Applying an elitist electromagnetism-like algorithm to head robot stabilization
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
A revised electromagnetism-like mechanism for layout design of reconfigurable manufacturing system
Computers and Industrial Engineering
An electromagnetism metaheuristic for solving the Maximum Betweenness Problem
Applied Soft Computing
Computers and Industrial Engineering
Journal of Visual Communication and Image Representation
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In global optimization, a typical population-based stochastic search method works on a set of sample points from the feasible region. In this paper, we study a recently proposed method of this sort. The method utilizes an attraction-repulsion mechanism to move sample points toward optimality and is thus referred to as electromagnetism-like method (EM). The computational results showed that EM is robust in practice, so we further investigate the theoretical structure. After reviewing the original method, we present some necessary modifications for the convergence proof. We show that in the limit, the modified method converges to the vicinity of global optimum with probability one.