A combined heuristic optimization technique

Schmidt, Hernán Prieto; Thierauf, Georg

doi:10.1016/j.advengsoft.2003.12.001

Cited by 39 publications

(11 citation statements)

References 11 publications

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“…The latter are usually slower but can find the true global minimum with high probability. There are also many different hybrid methods that try to exploit the fast convergence of the local methods and the global search capabilities of the global methods [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

Olenšek

Tuma

Puhan

et al. 2011

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

Olenšek

Tuma

Puhan

et al. 2011

Applied Soft Computing

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“…Finally, a descent method is applied to point out all global optima. Schmidt and Thierauf (2005) hybridised TA with DE. In first phase, TA was applied once to all individuals of a selected population.…”

Section: Introductionmentioning

confidence: 99%

Differential evolution and threshold accepting hybrid algorithm for unconstrained optimisation

Chauhan¹,

Ravi

2010

IJBIC

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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.

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“…But this simple approach worsens the diversity of the population and the robustness of the algorithm [31]. Other methods using improved rounding techniques involving some additional conditions, constraints, and thresholds were published by Angira and Babu [32], Liao [26], or Schmidt and Thierauf [33]. Tasgetiren et al introduced several approaches using a discrete DE algorithm for a flow shop scheduling problem [14, 34].…”

Section: Introductionmentioning

confidence: 99%

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

Uher

Gajdoš

Radecký

et al. 2016

Computational Intelligence and Neuroscience

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The Differential Evolution (DE) is a widely used bioinspired optimization algorithm developed by Storn and Price. It is popular for its simplicity and robustness. This algorithm was primarily designed for real-valued problems and continuous functions, but several modified versions optimizing both integer and discrete-valued problems have been developed. The discrete-coded DE has been mostly used for combinatorial problems in a set of enumerative variants. However, the DE has a great potential in the spatial data analysis and pattern recognition. This paper formulates the problem as a search of a combination of distinct vertices which meet the specified conditions. It proposes a novel approach called the Multidimensional Discrete Differential Evolution (MDDE) applying the principle of the discrete-coded DE in discrete point clouds (PCs). The paper examines the local searching abilities of the MDDE and its convergence to the global optimum in the PCs. The multidimensional discrete vertices cannot be simply ordered to get a convenient course of the discrete data, which is crucial for good convergence of a population. A novel mutation operator utilizing linear ordering of spatial data based on the space filling curves is introduced. The algorithm is tested on several spatial datasets and optimization problems. The experiments show that the MDDE is an efficient and fast method for discrete optimizations in the multidimensional point clouds.

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A combined heuristic optimization technique

Cited by 39 publications

References 11 publications

A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

A new asynchronous parallel global optimization method based on simulated annealing and differential evolution

Differential evolution and threshold accepting hybrid algorithm for unconstrained optimisation

Utilization of the Discrete Differential Evolution for Optimization in Multidimensional Point Clouds

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