Novel global harmony search algorithm for unconstrained problems

Zou, Dexuan; Gao, Liqun; Wu, Jian; Li, Steven

doi:10.1016/j.neucom.2010.07.010

Cited by 140 publications

(68 citation statements)

References 21 publications

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“…• HS and six variants: Chaos HS (CHS) [3], Mahdavi HS (MHS) [21]; Global-best HS (GHS) [25], Selfadaptative GSH (SGHS) [33], Intelligent Tunned HS (ITHS) [41]; and Novel Global HS (NGHS) [4];…”

Section: Resultsmentioning

confidence: 99%

“…In their approach, a new solution is proposed by making use of the best harmony vector. Zou et al [44] proposed the Novel Global Harmony Search (NGHS), which differs from traditional HS with regard to three aspects: in the NGHS, the HMCR (Harmony Memory Considering Rate) and PAR parameters are excluded; a mutation probability p m is used; finally, NGHS has a modified improvisation scheme, and it always replaces the worst harmony with the new one. Finally, Pan [26] proposed the Self-adaptive Global best Harmony Search (SGHS) approach, which has a new improvisation scheme based on GHS.…”

Section: Evolutionary Optimization Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Swarm-based Descriptor Combination and its Application for Image Classification

Mansano¹,

Matsuoka²,

Abiuzzi³

et al. 2014

ELCVIA

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In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach.

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Section: Resultsmentioning

confidence: 99%

Section: Evolutionary Optimization Backgroundmentioning

confidence: 99%

Swarm-based Descriptor Combination and its Application for Image Classification

Mansano¹,

Matsuoka²,

Abiuzzi³

et al. 2014

ELCVIA

View full text Add to dashboard Cite

show abstract

“…In this section, the performance of proposed algorithm is investigated to solve ten low-dimensional 0-1 knapsack problems, where these instances are taken from [36,37]. The required information about test instances such as dimension and parameters is listed in Table 1.…”

Section: Low-dimensional 0-1 Knapsack Problemsmentioning

confidence: 99%

“…are calculated. On the other hand, the performance of the proposed IBBA-RSS algorithm is compared with six different algorithms that are reported in [37]: NGHS1 [36], SBHS [37], BHS [38], DBHS [39], ABHS [40] and ABHS1 [41]. Table 2 shows the comparisons between the proposed algorithm and six algorithms, where best results are highlighted in bold.…”

Section: Low-dimensional 0-1 Knapsack Problemsmentioning

confidence: 99%

New binary bat algorithm for solving 0–1 knapsack problem

Rizk‐Allah

Hassanien

2017

Complex Intell. Syst.

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This paper presents a novel binary bat algorithm (NBBA) to solve 0-1 knapsack problems. The proposed algorithm combines two important phases: binary bat algorithm (BBA) and local search scheme (LSS). The bat algorithm enables the bats to enhance the exploration capability while LSS aims to boost the exploitation tendencies and, therefore, it can prevent the BBA-LSS from the entrapment in the local optima. Moreover, the LSS starts its search from BBA found so far. By this methodology, the BBA-LSS enhances the diversity of bats and improves the convergence performance. The proposed algorithm is tested on different size instances from the literature. Computational experiments show that the BBA-LSS can be promise alternative for solving large-scale 0-1 knapsack problems.

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“…In some instances, these optimization problems are non-differentiable, which is hard to solve for the gradient-based methods. Thus, researchers have to rely on some stochastic global optimization technologies such as particle swarm optimization algorithm (PSO) [1], differential evolution algorithm (DE) [2], genetic algorithm (GA) [3], harmony search algorithm (HS) [4,5] etc., because they do not need to compute the gradients of the objective function, and they do not require the continuity of problem variables. In consideration of the preferable advantages of these stochastic global optimization algorithms, researchers have payed closer attention to them recently, and they have implemented these technologies on many complex optimization problems including reliability problems [6,7], tile manufacturing process [8], adiabatic styrene reactor [9], Off-Centre bracing system [10], T-S fuzzy models [11] and so on.…”

Section: Introductionmentioning

confidence: 99%