Selective Search

Edelkamp, Stefan; Schrödl, Stefan

doi:10.1016/b978-0-12-372512-7.00014-6

Cited by 18 publications

(19 citation statements)

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“…Tabu Search (TS) is metaheuristic algorithm depends on the hill-climbing and scanning strategies to avoid earlier solutions. [21] II. TRAVELING SALESMAN PROBLEM (TSP):…”

Section: Overviewmentioning

confidence: 99%

“…Tabu search, created by Fred W. Glover in (1986) and formalized in (1989) [23].it is a metaheuristic search method employing local search methods used for solving mathematical optimization [21].…”

Section: Tabu Search (Ts)mentioning

confidence: 99%

“…In algorithm 1.1 the pseudo-code appears. [21] Any state visited during the previous k phase should be forbidden as single strategy to update the Tabu list. Additional alternative is to allow local alteration not to alert the same sections of the state path or to modify the search cost function.…”

Section: Tabu Search (Ts)mentioning

confidence: 99%

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Evaluation of EHO, U-TACO and TS Metaheuristics algorithms in Solving TSP

2020

JXAT

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Metaheuristics is one of the most well-known field of researches used to find optimum solution for a given optimization problem. Over time many algorithms have been developed based on the heuristics, this paper will compare three metaheuristic based algorithms "Elephant Herding optimization algorithm (EHO), U-Turning Ant colony optimization (U-TACO) and Tabu Search (TS)" to solve one of the most known problem in the optimization field knows as Traveling Salesman Problem (TSP). the experimental results of the paper compare the results of EHO, TS and U-TACO for solving 10 different problems from the TSPLIB95.

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“…Tabu Search (TS) is metaheuristic algorithm depends on the hill-climbing and scanning strategies to avoid earlier solutions. [21] II. TRAVELING SALESMAN PROBLEM (TSP):…”

Section: Overviewmentioning

confidence: 99%

Section: Tabu Search (Ts)mentioning

confidence: 99%

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Evaluation of EHO, U-TACO and TS Metaheuristics algorithms in Solving TSP

2020

JXAT

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“…The evolution strategies (ESs) represent one of the major paradigms of the evolutionary algorithm, used in a wide variety of optimization tasks and typically continuous but also discrete, combinatorial search spaces with and without constraints [28]. One of the main features of ESs is self-adaptability, a mechanism that adjusts the parameters of the exploration distribution during the search process [29]. In most of the cases, the self-adaptation is strictly related to the mutation mechanism.…”

Section: Evolution Strategiesmentioning

confidence: 99%

“…In this case, the genotype and the phenotype coincide and the representation of the chromosome is straightforward, with no codification being needed. If the search procedure implements self-adaptation, each candidate solution c is defined in terms of solution part c sol and parameter part c part [29]. For instance, in the case of the standard uncorrelated multistep mutation, the chromosome representation is as follows c = c sol , c part (20)…”

Section: Evolution Strategiesmentioning

confidence: 99%

New Evolutionary-Based Techniques for Image Registration

Cocianu¹,

Stan²

2019

Applied Sciences

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The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.

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