Multi-objective artificial bee algorithm based on decomposition by PBI method

Bai, Jian; Liu, Hong

doi:10.1007/s10489-016-0787-x

Cited by 31 publications

(5 citation statements)

References 31 publications

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“…Their main idea is to decompose an MOP into a number of scalaring sub-problems and solve them in parallel, where the decomposition method plays an extremely important role. There are three commonly used decomposition methods [23]: weighted sum approach (WS) [25], Tchebycheff approach (TCH) [25] and the penalty-based boundary intersection approach (PBI) [23,26], described as follows.…”

Section: Decomposition Methodsmentioning

confidence: 99%

Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition

Tan

Zheng

et al. 2018

Applied Sciences

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Decomposition-based multi-objective evolutionary algorithms provide a good framework for static multi-objective optimization. Nevertheless, there are few studies on their use in dynamic optimization. To solve dynamic multi-objective optimization problems, this paper integrates the framework into dynamic multi-objective optimization and proposes a memory-enhanced dynamic multi-objective evolutionary algorithm based on L p decomposition (denoted by dMOEA/D- L p ). Specifically, dMOEA/D- L p decomposes a dynamic multi-objective optimization problem into a number of dynamic scalar optimization subproblems and coevolves them simultaneously, where the L p decomposition method is adopted for decomposition. Meanwhile, a subproblem-based bunchy memory scheme that stores good solutions from old environments and reuses them as necessary is designed to respond to environmental change. Experimental results verify the effectiveness of the L p decomposition method in dynamic multi-objective optimization. Moreover, the proposed dMOEA/D- L p achieves better performance than other popular memory-enhanced dynamic multi-objective optimization algorithms.

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Section: Decomposition Methodsmentioning

confidence: 99%

Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition

Tan

Zheng

et al. 2018

Applied Sciences

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“…Much multiobjective research on ABCs has been proposed. Baiand and Liu proposed a multiobjective artificial bee colony (MOABC) algorithm based on decomposition by the penalty-based boundary intersection (PBI) method [27]. Additionally, considering the relative importance of objectives, Li, Lei, and Cai [28] addressed an energy-efficient HFSP with total tardiness, makespan, and total energy consumption.…”

Section: The Hybrid Flowshop Green Scheduling Problemmentioning

confidence: 99%

A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots

2023

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Hybrid flowshop scheduling problems are encountered in many real-world manufacturing scenarios. With increasingly fierce market competition, the production mode of multiple varieties and small batches has gradually been accepted by enterprises, where the technology of lot streaming is widely used. Meanwhile, green criteria, such as energy consumption and carbon emissions, have attracted increasing attention to improving protection awareness. With these motivations, this paper studies a multiobjective hybrid flowshop green scheduling problem with consistent sublots (MOHFGSP_CS), aiming to minimize two objectives, i.e., makespan and total energy consumption, simultaneously. To solve this complex problem, we first formulate a novel multiobjective optimization model. However, due to the NP-hard nature of the problem, the model is computationally prohibitive as the problem scale increases. Thus, a multiobjective discrete artificial bee colony algorithm (MDABC) based on decomposition is proposed. There are three phases in this algorithm: the VND-based employed bee phase, the adjustment weight onlooker bee phase, and the population interaction scout bee phase. In the experimental study, various small-scale and large-scale instances are collected to verify the effectiveness of the multiobjective optimization model and the MDABC. Comprehensive computational comparisons and statistical analysis show that the developed strategies and MDABC show superior performance.

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“…Objective space partitioning can improve the convergence by overcoming the problem only solutions closest to the reference points are considered in the niche-preservation operation phase. The PBI aggregation function [29] is used to replace the perpendicular distance and hence further improve the convergence of the NSGA-III. In this paper, the performance of the NSGA-III-GKM and other state-of-the-art algorithms is evaluated by the inverted generation distance (IGD) indicator [30], [31] and hypervolume (HV) indicator [32].…”

Section: The Proposed Nsga-iii-gkmmentioning

confidence: 99%

An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm

Liu

et al. 2019

IEEE Access

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The non-dominated sorting genetic algorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory. In order to improve the convergence, we propose an improved NSGA-III using a genetic K-means clustering algorithm (NSGA-III-GKM), which can also ensure diversity and automatically provide the number and direction vector of the subspaces. Compared with the NSGA-III, the proposed NSGA-III-GKM has two key features. First, the initial reference points are clustered using a GKM clustering algorithm, which realizes automatic learning of the number of clusters. Second, as the reference points are replaced by cluster centers, a penalty-based boundary intersection (PBI) aggregation function is introduced to replace the perpendicular distance. The proposed NSGA-III-GKM and other similar optimization algorithms (NSGA-III, MOEA/D, U-NSGA-III, DC-NSGA-III and B-NSGA-III) are tested on DTLZ test problems and UF test problems. The simulation results demonstrate that the NSGA-III-GKM exhibits better diversity and convergence performance than the other algorithms.INDEX TERMS Many-objective optimization, genetic K-means clustering algorithm, NSGA-III, automatic learning.

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Multi-objective artificial bee algorithm based on decomposition by PBI method

Cited by 31 publications

References 31 publications

Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition

Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition

A Multiobjective Optimization Approach for Multiobjective Hybrid Flowshop Green Scheduling with Consistent Sublots

An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm

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