Comparison of the performance of modern heuristics for combinatorial optimization on real data
Computers and Operations Research
Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Tabu search for fuzzy optimization and applications
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
Continuous interacting ant colony algorithm based on dense heterarchy
Future Generation Computer Systems - Special issue: Computational chemistry and molecular dynamics
An artificial immune network for multimodal function optimization on dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Memory Models for Improving Tabu Search with Real Continuous Variables
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
Tabu Search Solution for Fuzzy Linear Programming
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
Expert Systems with Applications: An International Journal
Optimization for signal setting problems using non-smooth techniques
Information Sciences: an International Journal
Efficient hybrid methods for global continuous optimization based on simulated annealing
Computers and Operations Research
Solving Fuzzy Linear Regression with Hybrid Optimization
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Structural-acoustic optimization of a rectangular plate: A tabu search approach
Finite Elements in Analysis and Design
Information Sciences: an International Journal
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Finding a global optimum of an unknown system has attracted a great deal of interest in many engineering problems. In this settings, meta-heuristics are very common as efficient approaches for solving complex real-world problems in global continuous optimization problems (GCOPs) as they can approximate solutions without considering mathematical constraints such as differentiability. In this study, we propose a method based on tabu search (TS) and Nelder-Mead (NM) search strategy in application to GCOPs. To increase the robustness of the proposed method, we add a new phase, referred to as partitioning phase, before diversification which is realized by the TS. The TS is improved and then followed by the NM search strategy. The partitioning phase aims at distributing initial random solutions in the search space. By doing this, we increase the robustness of the method. The TS has an interesting ability of covering a wide solution space by promoting the search far away from the current solution and consecutively decreasing the possibility of trapping in local minima. The neighbour search-strategy of the TS is improved to accelerate the speed of finding the near optimum solution. Instead of just generating random neighbours around the current solution, we generate some neighbours in the direction of the previous move as well as some neighbours in the previous best crown. When certain criteria are reached for the diversification of the search space, the NM search strategy is carried out with the focus on the intensification of the solution found in the diversification phase. We assess the performance of the algorithm for a range of standard test functions available in the literature and compare the obtained results with those available in the literature. There are two main advantages of the proposed method; first, it can apply to any GCOP without considering any constraints and secondly, it shows better performance (in terms of function evaluation, success rate, and average error) for the functions with less than four input variables and relatively small or medium input domains. In other cases, the method still has acceptable perfomance and produces the results that are comparable with the results produced by other methods.