A New Local Search-Based Multiobjective Optimization Algorithm

Chen, Bili; Zeng, Wenhua; Lin, Yangbin; Zhang, Defu

doi:10.1109/tevc.2014.2301794

Cited by 135 publications

(27 citation statements)

References 61 publications

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“…This section describes the proposed MOHH combined with a novel selection strategy: a quantum-inspired selection strategy. In the proposed framework, a pool of metaheuristics for scheduling, that is, SPEA2 [65], NSGA-II [66], nondominated sorting and local search (NSLS) [67], and bi-goal evolution (BiGE) [68], that has corresponding expertise, is developed as LLH. At high-level, a quantum-inspired learning strategy for the multiobjective problem is used to select promising metaheuristics and to maintain the diversity of choice.…”

Section: Proposed Hyper-heuristicmentioning

confidence: 99%

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

et al. 2019

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With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

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Section: Proposed Hyper-heuristicmentioning

confidence: 99%

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

et al. 2019

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“…For example, Wagner and Neumann [31] propose a fast Approximation-Guided Evolutionary multi-objective algorithm (AGE-II), which approximate the archive in order to control its size and influence on the runtime, and avoid the shortcomings of Approximation-Guided Evolution (AGE). Bili Chen and Wenhuan Zeng [15] propose the Non-dominated Sorting and Local Search (NSLS) optimization algorithm, which have better diversity in terms of combining the farthest-candidate approach with the non-dominated sorting method to select the new population members. But it easily falls into local optimum on some test problems.…”

Section: Research On Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

“…In recent years, there has also been a considerable amount of newly presented multi-objective optimization algorithms showing their competitiveness in the field of multi-objective optimization. Bili Chen proposed Non-dominated Sorting and Local Search (NSLS) [15], which was used to solve high-dimensional big data and it turned out that NSLS could find a better spread of solutions and better convergence to the true Pareto-optimal front [16]. Bi-Criterion Evolution for Indicator-Based Evolutionary Algorithm (BCE-IBEA) [17] and Multi-Objective Evolutionary Algorithm based on an enhanced Inverted Generational Distance metric (MOEA/IGD-NS) [18], and so forth, also show great potential for solving MOO problems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

2019

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Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.

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“…Let us consider that there were N Particles in Swarm to achieve optimal fitness. The Particle Best Position pbest and Global Best Position gbest need to update to attain and compute fitness [27][28][29][30].…”

Section: Multi-objective Particle Swarm Optimization (Mpso)mentioning

confidence: 99%

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

Subasree

Gopalan²,

Sakthivel

2018

IJET

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Microarray based Cancer Pattern Classification is one of the popular techniques in Bioinformatics Research. This Research Work is noticed that for studying the expression levels through the Gene Expression profiling experiments, thousands of Genes have to be simultaneously studied to understand the patterns of the Gene Expression or Cancer Pattern. This research work proposed an efficient Cancer Pattern Clas-sifier called An Enhanced Multi-Objective Pswarm (EMOPS) and it is studied thoroughly in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. The results were compared with the recently proposed classifiers namely Hybrid Ant Bee Algorithm (HABA), Kernelized Fuzzy Rough Set Based Semi Supervised Support Vector Machine (KFRS-S3VM) and Multi-objective Particle Swarm Optimization (MPSO). For analyzing the performances of the proposed model, this work considered a few cancer patterns namely Bladder, Breast, Colon, Endometrial, Kidney, Leukemia, Lung, Melanoma, Mom-Hodgkin, Pancreatic, Prostate and Thyroid. From our experimental results, it was noticed that the proposed model outperforms the identified three classifiers in terms of Memory Utilization, Execution Time (Processing Time), Sensitivity, Specificity, Classification Accuracy and FScore. To improve the performance of the system further in term of Processing Time, the proposed model Enhanced Multi-Objective Pswarm (EMOPS) is implemented under Parallel Framework and evaluated. That is the model is tested with Two, Four, Eight and Sixteen Parallel Processors and from the results, it is established that the Processing Time decreases considerably which will improve the performance of the Proposed Model.

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A New Local Search-Based Multiobjective Optimization Algorithm

Cited by 135 publications

References 61 publications

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

EMOPS: an enhanced multi-objective pswarm based classifier for poorly understood cancer patterns

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