Improved Bat Algorithm (IBA) on Continuous Optimization Problems

Yılmaz, Selim; Küçüksille, Ecir Uğur

doi:10.7763/lnse.2013.v1.61

Cited by 87 publications

(49 citation statements)

References 11 publications

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“…Furthermore, the survival process of bats is achieved based on the injective (one-to-one) strategy, where the fit one replaces the worst one based on feasibility rule. The performance of Convergence behavior for LKP 21 Convergence behavior for LKP 22 Convergence behavior for LKP 23 Convergence behavior for LKP 24 Convergence behavior for LKP 25 Convergence behavior for LKP 26 Convergence behavior for LKP 26 Convergence behavior for LKP 27 Convergence behavior for LKP 28 Convergence behavior for LKP 29 Convergence behavior for LKP 30 Convergence behavior for LKP 31 Table 9 Wilcoxon test for comparison results in Tables 5 and 6 Compared methods Solution evaluations…”

Section: Discussionmentioning

confidence: 99%

“…A hybrid BA based on harmony search for solving optimization problems was proposed by Wang and Guo [25]. Yilmaz and Kucuksille [26] developed an improved BA using some modifications, while Wang et al [27] presented a modified BA through adjusting the flight speed and the flight direction adaptively. Fister et al [28] introduced a new version of BA based on self-adaptation of control parameters.…”

Section: Introductionmentioning

confidence: 99%

“…The earlier applications showed that BA could solve different optimization problems and proved that its efficiency and robustness compared to different algorithms such as GA and PSO [20][21][22]. A new trend in bat algorithms is focusing on hybridizing BA with different strategies [23][24][25][26][27][28][29][30][31]. Fister et al [23] developed a hybrid BA based on various evolution strategies for solving optimization tasks, while Baziar et al [24] proposed a modified BA based on adaptive self-strategy.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New binary bat algorithm for solving 0–1 knapsack problem

Rizk‐Allah

Hassanien

2017

Complex Intell. Syst.

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“…Lin et al [58] presented another hybrid of Lévy flight and bat approach (CLBA) for parameter approximation in a nonlinear dynamic model. Yılmaz and Kucuksille [59], motivated by the PSO algorithm and the ABC algorithm, proposed an improved bat algorithm (IBA) to advance the exploration mechanism of the algorithm. Wang and Guo [60] integrated the harmony search (HS) method into BA, and developed a hybrid metaheuristic (HSBA) method to increase the convergence speed of BA.…”

Section: Bat Algorithm Methodsmentioning

confidence: 99%

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

2017

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Advanced global optimization algorithms have been continuously introduced and improved to solve various complex design optimization problems for which the objective and constraint functions can only be evaluated through computation intensive numerical analyses or simulations with a large number of design variables. The often implicit, multimodal, and ill-shaped objective and constraint functions in high-dimensional and "black-box" forms demand the search to be carried out using low number of function evaluations with high search efficiency and good robustness. This work investigates the performance of six recently introduced, nature-inspired global optimization methods: Artificial Bee Colony (ABC), Firefly Algorithm (FFA), Cuckoo Search (CS), Bat Algorithm (BA), Flower Pollination Algorithm (FPA) and Grey Wolf Optimizer (GWO). These approaches are compared in terms of search efficiency and robustness in solving a set of representative benchmark problems in smooth-unimodal, non-smooth unimodal, smooth multimodal, and non-smooth multimodal function forms. In addition, four classic engineering optimization examples and a real-life complex mechanical system design optimization problem, floating offshore wind turbines design optimization, are used as additional test cases representing computationally-expensive black-box global optimization problems. Results from this comparative study show that the ability of these global optimization methods to obtain a good solution diminishes as the dimension of the problem, or number of design variables increases. Although none of these methods is universally capable, the study finds that GWO and ABC are more efficient on average than the other four in obtaining high quality solutions efficiently and consistently, solving 86% and 80% of the tested benchmark problems, respectively. The research contributes to future improvements of global optimization methods.

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“…In [21], author proposed an Improved Bat Algorithm (IBA) for solving the insufficient performance of traditional BAT Algorithm. The author explored the exploitation and exploration strategies with three modifications.…”

Section: Mutation Strategiesmentioning

confidence: 99%

An Overview of Mutation Strategies in Bat Algorithm

Bangyal¹,

Ahmad²,

Tayyab³

et al. 2018

ijacsa

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Abstract-Bat algorithm (BA) is a population based stochastic search technique encouraged from the intrinsic manner of bee swarm seeking for their food source. BA has been mostly used to resolve diverse kind of optimization problems and one of major issue faced by BA is frequently captured in local optima meanwhile handling the complex real world problems. Many authors improved the standard BA with different mutation strategies but an exhausted comprehensive overview about mutation strategies is still lacking. This paper aims to furnish a concise and comprehensive study of problems and challenges that prevent the performance of BA. It has been tried to provide guidelines for the researchers who are active in the area of BA and its mutation strategies. The objective of this study is divided in two sections: primarily to display the improvement of BA with mutation strategies that may enhance the performance of standard BA up to great extent and secondly, to motivate the researchers and developers for using BA to solve the complex real world problems. This study presents a comprehensive survey of the various BA algorithms based on mutation strategies. It is anticipated that this survey would be helpful to study the BA algorithm in detail for the researcher.

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Improved Bat Algorithm (IBA) on Continuous Optimization Problems

Cited by 87 publications

References 11 publications

New binary bat algorithm for solving 0–1 knapsack problem

New binary bat algorithm for solving 0–1 knapsack problem

A Comparative Study on Recently-Introduced Nature-Based Global Optimization Methods in Complex Mechanical System Design

An Overview of Mutation Strategies in Bat Algorithm

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