New directional bat algorithm for continuous optimization problems

Chakri, Asma; Khelif, Rabia; Benouaret, Mohamed; Yang, Xin‐She

doi:10.1016/j.eswa.2016.10.050

Cited by 203 publications

(92 citation statements)

References 38 publications

(71 reference statements)

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“…Echolocation behavior of real bats has been inspired by Yang to propose a new meta‐heuristic technique for solving different types of combinatorial problems in 2010, which is called BA. The BA is a recent optimization algorithm using swarm intelligence and inspiration from the echolocation behavior of bats . The idealization of the echolocation of bats can be summarized as follows: each virtual bat flies randomly with a velocity v i at position (solution) x i with a varying frequency and loudness A i .…”

Section: Service Composition Strategiesmentioning

confidence: 99%

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

Navimipour

2018

Int J Communication

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Summary Many sorts of services in the cloud environments must be composited based on the user's requests to meet the requirements. Thus, the distributed services are joined to the cloud services through service composition. Also, it is known as NP‐hard problems and many researchers significantly are focused on this problem in recent years. Therefore, many different nature‐inspired meta‐heuristic techniques are proposed for solving this problem. The nature‐inspired meta‐heuristic techniques have an important role in solving the service composition problem in the cloud environments, but there is not a wide‐ranging and detailed paper about reviewing and studying the important mechanisms in this field. Therefore, this study presents a comprehensive analysis of the nature‐inspired meta‐heuristic techniques for the service composition issue in the cloud computing. The review also contains a classification of the important techniques. These classifications include Ant Colony Optimization, Bee Colony Optimization, Genetic Algorithm, Particle Swarm Optimization, Cuckoo Optimization Algorithm, Bat Algorithm, greedy algorithm, and hybrid algorithm. An important aim of this paper is to highlight the emphasis on the optimization algorithms, and the benefits to tackle the challenges are encountered in the cloud service composition. Also, this paper presents the advantages and disadvantages of the nature‐inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments. Moreover, this paper aims to provide more efficient service composition algorithms in the future. Finally, the obtained results have shown that the discussed algorithms have an important effect in solving the cloud service composition problem, and this effect has been increased in recent years.

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Section: Service Composition Strategiesmentioning

confidence: 99%

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Asghari

Navimipour

2018

Int J Communication

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“…Group Search Optimizer (GSO) is a type of swarm intelligence algorithm that simulates the behavior of animals in nature finding survival resources [44,45]. The homology between BA and GSO determines that they have natural fusion characteristics [42], although the hybrid algorithm is rarely seen at present.…”

Section: Improved Bat Algorithm For Resolving Drtmentioning

confidence: 99%

“…The bat algorithm (BA) is a swarm intelligence algorithm that simulates the echo-location behavior of bats in hunting prey [42,43]. Combining the BA with other intelligent algorithms is an effective way to avoid immature convergence of the BA.…”

Section: Improved Bat Algorithm For Resolving Drtmentioning

confidence: 99%

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

2018

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This paper presents a mixed-integer linear programming model for demand-responsive feeder transit services to assign vehicles located at different depots to pick up passengers at the demand points and transport them to the rail station. The proposed model features passengers' one or several preferred time windows for boarding vehicles at the demand point and their expected ride time. Moreover, passenger satisfaction that was related only to expected ride time is fully accounted for in the model. The objective is to simultaneously minimize the operation costs of total mileage and maximize passenger satisfaction. As the problem is an extension of the nondeterministic polynomial problem with integration of the vehicle route problem, this study further develops an improved bat algorithm to yield meta-optimal solutions for the model in a reasonable amount of time. When this was applied to a case study in Nanjing City, China, the mileage and satisfaction of the proposed model were reduced by 1.4 km and increased by 7.1%, respectively, compared with the traditional model. Sensitivity analyses were also performed to investigate the impact of the number of designed bus routes and weights of objective functions on the model performance. Finally, a comparison of Cplex, standard bat algorithm, and group search optimizer is analyzed to verify the validity of the proposed algorithm.

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“…Another modification to the BA was proposed by Yang et al in 2017, which consist in the application of a directional echolocation with the objective of improving the exploration and exploitation capabilities to avoid premature convergence in [39].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

et al. 2017

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Abstract:In this paper, a comparison among Particle swarm optimization (PSO), Bee Colony Optimization (BCO) and the Bat Algorithm (BA) is presented. In addition, a modification to the main parameters of each algorithm through an interval type-2 fuzzy logic system is presented. The main aim of using interval type-2 fuzzy systems is providing dynamic parameter adaptation to the algorithms. These algorithms (original and modified versions) are compared with the design of fuzzy systems used for controlling the trajectory of an autonomous mobile robot. Simulation results reveal that PSO algorithm outperforms the results of the BCO and BA algorithms.

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New directional bat algorithm for continuous optimization problems

Cited by 203 publications

References 38 publications

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Nature inspired meta‐heuristic algorithms for solving the service composition problem in the cloud environments

Optimal Design of Demand-Responsive Feeder Transit Services with Passengers’ Multiple Time Windows and Satisfaction

Comparative Study of Type-2 Fuzzy Particle Swarm, Bee Colony and Bat Algorithms in Optimization of Fuzzy Controllers

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