An effective invasive weed optimization algorithm for scheduling semiconductor final testing problem

Sang, Hongshi; Duan, Peiyong; Li, Junqing

doi:10.1016/j.swevo.2017.05.007

Cited by 69 publications

(38 citation statements)

References 40 publications

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“…Also, many other researchers have proposed other state-of-the-art metaheuristic algorithms, such as particle swarm optimization (PSO) [51][52][53][54][55][56], cuckoo search (CS) [57][58][59][60][61], probability-based incremental learning (PBIL) [62], differential evolution (DE) [63][64][65][66], evolutionary strategy (ES) [67,68], monarch butterfly optimization (MBO) [20], firefly algorithm (FA) [69][70][71][72], earthworm optimization algorithm (EWA) [73], genetic algorithms (GAs) [74][75][76], ant colony optimization (ACO) [77][78][79], krill herd (KH) [37,80,81], invasive weed optimization [82][83][84], stud GA (SGA) [85], biogeography-based optimization (BBO) [86,87], harmony search (HS) [88][89][90], and bat algorithm (BA) [91,92], among others Besides benchmark evaluations [93,…”

Section: Related Workmentioning

confidence: 99%

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

et al. 2019

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Inspired by the behavior of elephants in nature, elephant herd optimization (EHO) was proposed recently for global optimization. Like most other metaheuristic algorithms, EHO does not use the previous individuals in the later updating process. If the useful information in the previous individuals were fully exploited and used in the later optimization process, the quality of solutions may be improved significantly. In this paper, we propose several new updating strategies for EHO, in which one, two, or three individuals are selected from the previous iterations, and their useful information is incorporated into the updating process. Accordingly, the final individual at this iteration is generated according to the elephant generated by the basic EHO, and the selected previous elephants through a weighted sum. The weights are determined by a random number and the fitness of the elephant individuals at the previous iteration. We incorporated each of the six individual updating strategies individually into the basic EHO, creating six improved variants of EHO. We benchmarked these proposed methods using sixteen test functions. Our experimental results demonstrated that the proposed improved methods significantly outperformed the basic EHO.

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Section: Related Workmentioning

confidence: 99%

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

et al. 2019

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“…In recent years, inspired by natural phenomena, a variety of novel meta-heuristic algorithms have been reported, e.g., bat algorithm (BA) [23], amoeboid organism algorithm [24], animal migration optimization (AMO) [25], artificial plant optimization algorithm (APOA) [26], biogeography-based optimization (BBO) [27,28], human learning optimization (HLO) [29], krill herd (KH) [30][31][32], monarch butterfly optimization (MBO) [33], elephant herding optimization (EHO) [34], invasive weed optimization (IWO) algorithm [35], earthworm optimization algorithm (EWA) [36], squirrel search algorithm (SSA) [37], butterfly optimization algorithm (BOA) [38], salp swarm algorithm (SSA) [39], whale optimization algorithm (WOA) [40], and others. A review of swarm intelligence algorithms can be referred to [41].…”

Section: Of 31mentioning

confidence: 99%

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Feng

Wang

2019

Mathematics

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As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.

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“…The algorithm determines test schedules, machine setups, and job assignments. Sang et al [45] proposed a cooperative coevolutionary invasive weed optimization algorithm for a semiconductor final testing scheduling problem. The algorithm iterates with two coupled colonies, one of which addresses the machine assignment problem, while the other deals with the operation sequence problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Simulation Study for Semiconductor Manufacturing System: Dispatching Policies for a Wafer Test Facility

Yoon

Chae

2019

Sustainability

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The manufacture of semiconductor products requires many dedicated steps, and these steps can be grouped into several major phases. One of the major steps found at the end of the wafer fabrication process is the electrical die sorting (EDS) test operation. This paper focuses on dispatching policies in an EDS test facility to reduce unnecessary work for the system. This allows the semiconductor manufacturing facility to achieve better overall efficiency, thereby contributing to sustainable manufacturing by reducing material movements, the use of testing machines, energy consumption, and so on. In the facility, wafer lots are processed on a series of workstations (cells), and the facility holds identical parallel machines. The wafers are moved by an automatic material handling system from cell to cell as well as within cells. We propose several scheduling policies consisting of intercell and intracell material movements for efficient system operation. For this, four intercell scheduling policies and two intracell scheduling policies are introduced, and the effects of combinations are tested and evaluated through simulation experiments to obtain performance measures such as cycle time and work in process. The most efficient results among the combinations are presented as a proposed scheduling policy for a given EDS test facility.

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An effective invasive weed optimization algorithm for scheduling semiconductor final testing problem

Cited by 69 publications

References 40 publications

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems

A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Simulation Study for Semiconductor Manufacturing System: Dispatching Policies for a Wafer Test Facility

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