Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling

Ma, Haiping; Su, Shufei; Simon, Dan; Fei, Minrui

doi:10.1016/j.engappai.2015.05.009

Cited by 58 publications

(31 citation statements)

References 38 publications

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“…In this experiment, we integrate the cross-subpopulation migration into the established multi-objective biogeography-based optimization (MBBO) [36], multiobjective evolutionary algorithm based on decomposition (MOEA/D) [37], and multiple trajectory search (MTS) algorithm [38] under the condition of the original algorithm. Here, we choose MBBO because it is one of the most recent MOEAs.…”

Section: Optimization For Benchmarksmentioning

confidence: 99%

“…To obtain fair comparisons, a population size for the standard versions of these algorithms is set to 200. For the multi-population MOEA/D, we use the same weights for these subpopulations, and other parameter settings of these algorithms are referred to the original references [36][37][38]. The benchmark functions are compared by implementing discretized coding of all the standard and multi-population algorithms.…”

Section: Optimization For Benchmarksmentioning

confidence: 99%

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A Multipopulation-Based Multiobjective Evolutionary Algorithm

Fei

Jiang

et al. 2020

IEEE Trans. Cybern.

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Abstract-Multi-population is an effective optimization component often embedded into evolutionary algorithms to solve optimization problems. In this paper, a new multi-population based multi-objective genetic algorithm is proposed, which uses a unique cross-subpopulation migration process inspired by biological processes to share information between subpopulations. Then, a Markov model of the proposed multi-population multi-objective genetic algorithm is derived, the first of its kind, which provides an exact mathematical model for each possible population occurring simultaneously with multiple objectives. Simulation results of two multi-objective test problems with multiple subpopulations justify the derived Markov model, and show that the proposed multi-population method can improve the optimization ability of the multi-objective genetic algorithm. Also, the proposed multi-population method is applied to other multi-objective evolutionary algorithms for evaluating its performance against the IEEE Congress on Evolutionary Computation multi-objective benchmarks. The experimental results show that a single-population multi-objective evolutionary algorithm can be extended to a multi-population version, while obtaining better optimization performance.

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Section: Optimization For Benchmarksmentioning

confidence: 99%

Section: Optimization For Benchmarksmentioning

confidence: 99%

A Multipopulation-Based Multiobjective Evolutionary Algorithm

Fei

Jiang

et al. 2020

IEEE Trans. Cybern.

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“…Chutima and Naruemitwong (2014) implemented Pareto-BBO algorithm to solve mixed-model sequencing problems on a two-sided assembly line by considering three conflicting objectives. Ma et al (2015) proposed an ensemble multi-objective BBO (EMBBO) algorithm to solve the automated warehouse scheduling problem. The EMBBO algorithm constitutes vector evaluated BBO (VEBBO), NSBBO and niched Pareto BBO (NPBBO).…”

Section: Application Of Bbo and Nsbbo From Literaturementioning

confidence: 99%

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

Singh¹,

Ingole²

2019

10.5267/j.ijiec

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Quantitative and qualitative objectives are both significant to solve any facility layout problem (FLP), which is called as multi-objective FLP. Generally, quantitative factors are considered as material handling cost, time, etc., and qualitative factors are considered as closeness rating, hazardous movement between the facilities, etc. For solving and optimizing two or more objectives, two methods are available. First is weight approach method and second is nondominated sorting method. In the former method, suitable weights are given to each objective and combined in a single objective function; while in later method, the objectives are defined separately and by making comparison of the solutions on the non-dominance criteria, best Pareto-optimal solutions are obtained. In this paper, equal area multi-objective FLPs which are formulated as quadratic assignment problem (QAP) are considered and optimized using biogeography based optimization (BBO) algorithm and non-dominated sorting BBO (NSBBO) algorithm. BBO is one of the efficient metaheuristic techniques, developed to solve complex optimization problems. Computational results of BBO algorithm using weight approach illustrate its better performance compared to other methods while solving multi-objective FLPs. Furthermore to obtain Pareto optimal solutions, NSBBO algorithm is implemented.

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“…An efficient multi-objective optimization algorithm using the differential evolution (DE) algorithm is proposed to solve multi-objective optimal power flow (MO-OPF) problems [37]. BBO has also been modified to solve multi-objective optimization problems (MOPs) [38][39][40][41][42][43][44][45], such as, multi-objective biogeography-based optimization based on predator-prey approach [38], indoor wireless heterogeneous networks planning [39], automated warehouse scheduling [40], and community detection in social networks with node attributes [41]. Work in the literature [42] is focused on numerical comparisons of migration models for multi-objective biogeography-based optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

2017

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In order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in which the habitat suitability index (HSI) is redefined, based on the Pareto dominance relation, and density information among the habitat individuals. Then, we design a new algorithm, in which the modification probability and mutation probability are changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. The mutation operators based on DE algorithm, are modified, and the migration operators based on number of iterations, are improved to achieve better convergence performance. Numerical experiments on different ZDT and DTLZ benchmark functions are performed, and the results demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution properties comparing to the other MOEAs, and can solve more complex multi-objective optimization problems efficiently. distribution of solutions [6]. Second, there are several good results based on the hybridization of BBO with differential evolution (DE) [8,9]. Last, blend combination operators have been widely and successfully used in other population-based optimization algorithms [10].The original contributions of this paper include the following. Firstly, the model of applying BBO for multi-objective evolutionary algorithm was established, in which the habitat suitability index is redefined, based on the Pareto dominance relation and density information among the habitat individuals. Motivated by the work in [11,12], we hybridize adaptive BBO with DE to design a new algorithm for multi-objective optimization problems. In this algorithm, the mutation operator based on DE algorithm was modified, and the migration operator based on the number of iterations was re-designed to achieve better performance. Furthermore, the modification probability and mutation probability are adaptively changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. Experiments performed on ZDT and DTLZ benchmark functions show that the obtained Pareto solution set can approximate to the Pareto optimal front, and has good diversity and uniform distribution. The results also demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution.The paper is organized as follows. Section 2 describes the related work about BBO algorithm. The basic theories of multi-objective optimization are presented in Section 3.The MABBO algorithm procedure for multi-objective optimization problems are proposed in Section 4. Section 5 shows the simulation and experiment results. ...

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Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling

Cited by 58 publications

References 38 publications

A Multipopulation-Based Multiobjective Evolutionary Algorithm

A Multipopulation-Based Multiobjective Evolutionary Algorithm

Multi-objective facility layout problems using BBO, NSBBO and NSGA-II metaheuristic algorithms

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

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