The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

Li, Mingfu; Zhang, Yuyan; Zeng, Bing; Zhou, Houming; Jin-gang, Liu

doi:10.1007/s00170-015-7457-8

Cited by 25 publications

(13 citation statements)

References 47 publications

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“…The first one is using the same updating formula and changing the results to discrete values afterwards [82,94,104]. And the second is to modify the updating formula on the discrete space [97][98][99][100][101]. The first problem is susceptible of trapping in local solution and misses the optimal solution.…”

Section: Discussionmentioning

confidence: 99%

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A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Khan¹,

Hamadneh²,

Tilahun³

et al. 2016

Optimization Algorithms - Methods and Applications

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Firefly algorithm is one of the well-known swarm-based algorithms which gained popularity within a short time and has different applications. It is easy to understand and implement. The existing studies show that it is prone to premature convergence and suggest the relaxation of having constant parameters. To boost the performance of the algorithm, different modifications are done by several researchers. In this chapter, we will review these modifications done on the standard firefly algorithm based on parameter modification, modified search strategy and change the solution space to make the search easy using different probability distributions. The modifications are done for continuous as well as non-continuous problems. Different studies including hybridization of firefly algorithm with other algorithms, extended firefly algorithm for multiobjective as well as multilevel optimization problems, for dynamic problems, constraint handling and convergence study will also be briefly reviewed. A simulationbased comparison will also be provided to analyse the performance of the standard as well as the modified versions of the algorithm. . IntroductionAn optimization problem refers to the maximization or minimization of an objective function by setting suitable values for the variables from a set of feasible values. These problems appear not only in complex scientific studies but also in our day-to-day activities. For instance, when a person wants to go from one place to another and has multiple possible routes, a decision needs to be made on which route to take. The decision can be with the objective to minimize travel time, fuel consumption and so on. However, these kinds of problems with fewer number of alternatives can easily be solved by looking at the outcome of each of the alternatives. However, in real problems, it is not always the case to have a finite and small number of alternatives. Hence, different solution methods are proposed based on the behaviour of the problem. Firefly algorithm is among those metaheuristic algorithms which have different applications. Its uncomplicated and easy steps with its effectiveness attract researchers from different disciplines it. Different studies have been performed to modify the standard firefly algorithm to boost its performance and to make it suitable for a problem at hand. In this chapter, a comprehensive study will be presented on firefly algorithm and its modified versions. A brief discussion on extended firefly algorithm with other relevant studies will also be provided. In the next section, a discussion on optimization problems with their solution methods will be given followed by a review on studies on firefly algorithm, which includes a discussion on the standard firefly algorithm with its modified versions and other relevant studies on firefly algorithm, in Section . In Section , a comparative study based on simulation results will be presented followed by summary of the chapter in Section . . Optimization problemsDecision-making problems can be found ...

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Section: Discussionmentioning

confidence: 99%

“…Another modification in this category is proposed in [101]. The decision variables, x i 's, represent assembly sequence.…”

Section: 5itrmentioning

confidence: 99%

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Khan¹,

Hamadneh²,

Tilahun³

et al. 2016

Optimization Algorithms - Methods and Applications

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“…In this section, we introduce an assembly model which takes some important assembly factors into account, including the geometric constraints, the stability of assembly, the changes of assembly direction, the assembly tool changes, and the process precedence. In the next section, a brief introduction of this model is presented, and for more detail you can found it in Zeng et al [33] and Li et al [34].…”

Section: Assembly Modelingmentioning

confidence: 99%

“…A sequence violating the precedence process rule demands extra fixtures and more skilled workers. For this reason, the APPR matrix T is defined by Li et al [34] to represent the assembly process precedence of a mechanical product, shown as Eq. 3.…”

Section: Precedence Analysis Of the Assembly Processmentioning

confidence: 99%

Assembly sequence planning based on an improved harmony search algorithm

Qin

Zeng

et al. 2015

Int J Adv Manuf Technol

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As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability.

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“…The main objective is to enhance the design life of components such as box type heat exchangers and radiators. Furthermore, the PSO and FFA are also applied to optimize suitable process parameters that yield better tensile strength [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Realizing a Novel Friction Stir Processing-Enabled FWTPET Process for Strength Enhancement Using Firefly and PSO Methods

et al. 2020

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The friction welding of tube to tube plate using an external tool (FWTPET) is widely deployed in several industrial applications, such as aerospace, automotive, and power plants. Moreover, for achieving a better tensile strength and hardness in the weld zone, the friction stir processing (FSP) technique was incorporated into the FWTPET process for joining aluminum alloys (AA6063 tube, AA6061 tube plate). Furthermore, it has to be noted that FWTPET was applied for joining the AA6063 tube to the AA6061 tube plate, and FSP was deployed for reinforcing the weld zone with carbon nanotube (CNT) and silicon nitride (Si3N4) particles, thereby attaining the desirable mechanical properties. Subsequently, the Taguchi L25 orthogonal array was used for identifying the most influential input and output FWTPET + FSP process parameters. Furthermore, particle swarm optimization (PSO) and the firefly algorithm (FFA) were deployed for determining the optimized input and output FWTPET + FSP process parameters. The input process parameters include CNT, Si3N4, rotational tool speed, and depth. Furthermore, the tensile strength of the welded joint was considered as the output process parameter. The process parameters predicted by PSO and FFA were compared with the experimental values. It was witnessed that deviation between the predicted and experimental values was minimal. Moreover, it was found that FFA provided a superior tensile strength prediction than PSO.

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The modified firefly algorithm considering fireflies’ visual range and its application in assembly sequences planning

Cited by 25 publications

References 47 publications

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

A Review and Comparative Study of Firefly Algorithm and its Modified Versions

Assembly sequence planning based on an improved harmony search algorithm

Realizing a Novel Friction Stir Processing-Enabled FWTPET Process for Strength Enhancement Using Firefly and PSO Methods

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