Nonlinear programming: theory, algorithms, and applications
Nonlinear programming: theory, algorithms, and applications
Journal of Computational Physics
Global optimization and simulated annealing
Mathematical Programming: Series A and B
A deterministic algorithm for global optimization
Mathematical Programming: Series A and B
Journal of Computational Physics
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Journal of Global Optimization
A novel metaheuristics approach for continuous global optimization
Journal of Global Optimization
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Ant Colony Optimization
A combined heuristic optimization technique
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
Journal of Global Optimization
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy global optimization of complex system reliability
IEEE Transactions on Fuzzy Systems
A modified harmony search threshold accepting hybrid optimization algorithm
MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
International Journal of Computer Applications in Technology
Design of wide-beam antenna using dynamic multi-objective BBO/DE
International Journal of Computer Applications in Technology
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This paper develops a hybrid global optimisation metaheuristic methodology for solving unconstrained optimisation problems. The hybrid, to be called DETA, comprises two global optimisation algorithms viz. differential evolution (DE) and threshold accepting (TA) in tandem. While working with DE on benchmark problems, we noticed that it slows down before convergence is achieved. After analysing the possible reason for this shortcoming, we propose DETA to address it. DETA works in two phases: Phase 1 implements the original DE with relaxed convergence criterion. Then, a switch over is made from Phase 1 to Phase 2, where TA is used to quickly guide the search to global optimum. Performance of DETA is compared with that of DE on 26 unconstrained benchmark problems. The results obtained indicate that DETA hybrid is much superior to DE in terms of speed for the same level of accuracy.