Improving NSGA-II for solving multi objective function optimization problems

Vachhani, Vimal L.; Dabhi, Vipul K.; Prajapati, Harshadkumar B.

doi:10.1109/iccci.2016.7479921

Cited by 18 publications

(5 citation statements)

References 10 publications

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“…It was also used in hybrid with the crowding distance [25]. Instead of removing points with the smallest crowding distances in the NSGA-II, the diversity preservation way via clustering from SPEA [26] was used to select the survived individuals [27], [28]. However, none of these alternatives is as accepted in practice as the NSGA-II.…”

Section: Introductionmentioning

confidence: 99%

Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency for Many Objectives

Zheng,

Doerr

2024

IEEE Trans. Evol. Computat.

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The NSGA-II is one of the most prominent algorithms to solve multi-objective optimization problems. Despite numerous successful applications, several studies have shown that the NSGA-II is less effective for larger numbers of objectives. In this work, we use mathematical runtime analyses to rigorously demonstrate and quantify this phenomenon. We show that even on the simple m-objective generalization of the discrete OneMinMax benchmark, where every solution is Pareto optimal, the NSGA-II also with large population sizes cannot compute the full Pareto front (objective vectors of all Pareto optima) in subexponential time when the number of objectives is at least three. The reason for this unexpected behavior lies in the fact that in the computation of the crowding distance, the different objectives are regarded independently. This is not a problem for two objectives, where any sorting of a pair-wise incomparable set of solutions according to one objective is also such a sorting according to the other objective (in the inverse order).

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Section: Introductionmentioning

confidence: 99%

Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency for Many Objectives

Zheng,

Doerr

2024

IEEE Trans. Evol. Computat.

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“…One problem, however, with evolutionary algorithms like NSGA is the poor algorithm efficiency measured in terms of the total number of evaluations required for convergence to the true optimal frontier due to getting stuck on local optimal solutions (Sindhya et al, 2011). Care should be taken to ensure diversity of solutions with a view toward obtaining a wide variety of options for decision makers to choose from (Vachhani et al, 2016), as well as fine-tuning the parameters for the various traditional genetic algorithm operators to converge to optimal solutions faster (Pakath & Zaveri, 1995). As noted in Saborido et al (2016), the nature of the PPS problem may cause a fair share of infeasible solutions to be generated that may necessitate the extensive use of repair mechanisms.…”

Section: Solution Methodologymentioning

confidence: 99%

A generalized decision support framework for large‐scale project portfolio decisions

et al. 2021

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Project portfolio selection (PPS) is a complex problem faced by major companies whenever there are multiple funding opportunities with insufficient budget to fund them all. In this paper, we present our work on a PPS decision support tool that has become a fundamental part of the project portfolio decision process at Intel Corporation across its largest product and market divisions. The paper builds on a previous publication that outlines the decision support tool's bicriteria optimization model by providing a solution procedure that is capable of solving real-life PPS problems within time frames acceptable to decision makers, as well as providing further details on the data collection and the decision-making process. We also report on various analysis and visualization tools that have been built to allow decision makers to interact with promising solutions provided by the decision support tool. One of the contributions of the paper is to present a typology of the important dependencies between projects that needs to be considered, and provide details on how they are incorporated in the decision support tool's optimization engine. We discuss important implementation details on the decision-making process and the agents involved, and conclude by describing real-life experiences on how the framework can enable intuitive decision-making when choosing portfolios that best satisfy the organization's business goals.

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“…Paul and Shill [51] proposed an NSGA-II to optimize the two validity indices of the clustering processes, and the results reflected that the proposed NSGA-II has good performance in both gene representation and nongene representation datasets. Vachhani et al [52] proposed a new agglomerative hierarchical clustering process to accelerate the convergence process of the NSGA-II, and the experiments on the standard test of the multiobjective evolutionary algorithm showed that the improved NSGA-II is better than NSGA-II in terms of the existing diversity method. Due to the high efficiency in solving multiobjective optimization problems, the NSGA-II has been widely used and improved to solve the model for the design and optimization of the logistics network.…”

Section: Relevant Solution Methods For the 2e-cmdpdtwmentioning

confidence: 99%

Two-Echelon Multidepot Logistics Network Design with Resource Sharing

Luo

Wang

Tang

et al. 2021

Journal of Advanced Transportation

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Resource sharing within a logistics network offers an effective way to solve problems resulting from inefficient and costly operations of individual logistics facilities. However, the existing analysis of resource sharing and profit allocation is still limited. Therefore, this study aims to model resource sharing in two-echelon delivery and pickup logistics networks to improve the overall efficiency and decrease the total network operating cost. A bi-objective integer programming model is first proposed for two-echelon collaborative multidepot pickup and delivery problems with time windows (2E-CMDPDTW) to seek the minimization of operating costs and number of vehicles. Integrating a customer clustering algorithm, a greedy algorithm, and an improved nondominated sorting genetic algorithm-II (Im-NSGA-II), a hybrid method is then designed to handle the 2E-CMDPDTW model. The customer clustering and the greedy algorithms are employed to generate locally optimized initial solutions to accelerate the calculating velocity and guarantee the diversity of feasible solutions. The Im-NSGA-II combines the order crossover operation and the polynomial mutation process to find the optimal solution of the 2E-CMDPDTW. The comparative results show that the proposed hybrid method outperforms the NSGA-II and the multiobjective genetic algorithm. Furthermore, a Shapley value method is used for allocating total profits of established alliances and finding an optimal coalition sequence of the logistics facilities joining alliances based on the strictly monotonic path strategy. Finally, a case study of 2E-CMDPDTW in Chongqing China is conducted to validate the feasibility. Results indicate that this study contributes to long-term partnerships between logistics facilities within multi-echelon logistics networks in practice and contributes to the long-term sustainability of urban logistics pickup and delivery networks’ development.

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Improving NSGA-II for solving multi objective function optimization problems

Cited by 18 publications

References 10 publications

Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency for Many Objectives

Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency for Many Objectives

A generalized decision support framework for large‐scale project portfolio decisions

Two-Echelon Multidepot Logistics Network Design with Resource Sharing

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