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Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期107卷 104531-104531页

作者： Jimenez, Fernando Sanchez, Gracia Palma, Jose Sciavicco, Guido Univ Murcia Dept Informat & Commun Engn Murcia Spain Univ Ferrara Dept Math & Comp Sci Ferrara Italy

The large amount of data that is produced today with new technologies is an impediment for machine learning algorithms to work correctly, both due to the memory requirements and the necessary execution times. That is why the processes of reducing both the quantity and the size of the data are increasingly important. One of these processes is the so-called instance selection. In this paper we propose three-objective constrained optimization models to formulate instance selection wrapper and filter methods (separately) for classification problems, which are solved with multi-objective evolutionary algorithms and multi-objective differential evolution. In the proposed instance selection wrapper method, an objective is added to the usual ones to minimize the generalization error of the classifier. The proposed instance selection filter method simultaneously optimizes the correlation, redundancy and consistency of the datasets. Instance retention constraints are imposed on optimization models to retain a maximum percentage of samples, established by the decision maker, in big data scenarios. The experiments have been designed to compare (1) the NSGA-II and MODE algorithms, (2) two- and three-objective optimization models, (3) two different constraint handling techniques, and (4) the proposed evolutionary approaches and other 12 non-evolutionary approaches used in literature. The proposed wrapper and filter instance selection methods have been used in a real-world business engineering application, and have also been validated using three public datasets to facilitate the replicability of the research results. The results of the experiments show the superiority of the three-objective constrained evolutionary techniques proposed in this paper over the non-evolutionary techniques and over the two-objective evolutionary approaches used in the literature.

关键词： multi-objective evolutionary algorithms multi-objective constrained optimization Instance selection Classification

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A comparison between metaheuristics for solving a capacitated fixed charge transportation problem with multiple objectives

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 170卷 114491-114491页

作者： Biswas, Amiya Pal, Tandra ABN Seal Coll Cooch Behar Dept Math Cooch Behar 736101 India Natl Inst Technol Durgapur Dept CSE Durgapur India

The main focus of this work is to formulate and solve a Fixed Charge Transportation Problem (FCTP) considering multiple modes of transport with different capacities. The condition for the feasibility is that the total capacity of all the modes of transport at each origin must be at least the total quantity of commodity available at the origin. The problem is modeled as a multi-objective minimization problem with two conflicting objectives: the total transportation cost and the total transportation time. For this purpose, crossover and mutation operators have been designed to make them suitable for the problem. The problem is solved by a modified NSGA-II, obtained by adapting the newly developed genetic operators in the metaheuristic structure of NSGA-II. Five numerical example problems of various sizes are solved using the modified NSGA-II. For a fair comparison of the perfor-mance of the modified NSGA-II, the same example problems are solved using two other algorithms: modified SPEA-2 and a modified GrEA, obtained by incorporating the same newly developed crossover and mutation respectively into SPEA-2 and GrEA. The results obtained by the modified NSGA-II, modified SPEA-2 and modified GrEA are compared using four performance metrics: RNI value, HV, Spacing and GS. The modified NSGA-II performs best for all example problems according to the metrics RNI value and GS, and for all the example problems except the example Problem 3 according to the metrics HV and spacing. For the example Problem 3, the modified GrEA yields the best performance with respect to the metrics HV and spacing. The modified SPEA2 performs worst in terms of all the metric values for all the example problems. Finally, some potential future research directions are discussed in the conclusion.

关键词： Fixed Charge Transportation Problem multi-objective constrained optimization Metaheuristics Near Pareto set

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Research on modelling and optimization of hot rolling scheduling

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第3期10卷 1201-1216页

作者： Liu, Li-Lan Wan, Xiang Gao, Zenggui Li, Xiaolong Feng, Bowen Shanghai Univ Shanghai Key Lab Intelligent Mfg & Robot Yanchang Str 149 Shanghai Peoples R China

multiple species and random change in batch order forms has become the core business of Steel Intelligent Production Enterprises, necessitating new requirements for scheduling real-time adaptability and precision in the steel scheduling model. In this paper, a digital twin intelligent agent with cyberspace physical space integration is proposed as the unified driving source. First, an Open multiple objective Travelling Salesmen Problem model was established. For constraints such as a minimum rolling unit plan, process specification and a minimum power consumption per ton of steel, the NSGA-II algorithm was used to obtain a Pareto front for these constraints. With the help of appropriate penalty coefficients for the Pareto front, the constraints were defined as 3D-coordinates among cities of the Travelling Salesmen Problem. Combining the simulated annealing (SA) algorithm and the multi-objective particle swarm optimization (PSO) algorithm, the PSO algorithm was redefined and modified by introducing the metropolis criterion of the SA algorithm twice. This was used to obtain two extremes of the particle swarm, including the individual optimal solution and the global optimal solution to avoid local extrema. A model based on SA-MOPSO was thus obtained. Then, with the help of real-time mapping between the steel production line and the information model, the double-flow digital twin agent was established. This agent can achieve production scheduling model dynamic development and maturation itself. Finally, taking the steel hot rolling production line as a case in point, training and trial application of the model were performed to verify its adaptive development ability. Simulation results show that the algorithm discussed in this paper can solve the problem of hot rolling scheduling and provide support for decision-makers.

关键词： Digital twin Hot rolling plan scheduling multi-objective constrained optimization NSGA-II algorithm MOPSO algorithm

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An Improved Particle Swarm Algorithm for multi-objectives Based optimization in MPLS/GMPLS Networks

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IEEE ACCESS 2019年 7卷 137147-137162页

作者： Masood, Mohsin Fouad, Mohamed Mostafa Kamal, Rashid Glesk, Ivan Khan, Imran Ullah Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Lanark Scotland Arab Acad Sci Technol & Maritime Transport Cairo 12577 Egypt Abasyn Univ Dept Comp & Technol Peshawar 25000 Pakistan Harbin Engn Univ Coll Underwater Acoust Commun Engn Harbin 150001 Heilongjiang Peoples R China

Particle swarm optimization (PSO) is a swarm-based optimization technique capable of solving different categories of optimization problems. Nevertheless, PSO has a serious exploration issue that makes it a difficult choice for multi-objectives constrained optimization problems (MCOP). At the same time, multi-Protocol Label Switched (MPLS) and its extended version Generalized MPLS, has become an emerging network technology for modern and diverse applications. Therefore, as per MPLS and Generalized MPLS MCOP needs, it is important to find the Pareto based optimal solutions that guarantee the optimal resource utilization without compromising the quality of services (QoS) within the networks. The paper proposes a novel version of PSO, which includes a modified version of the Elitist learning Strategy (ELS) in PSO that not only solves the existing exploration problem in PSO, but also produces optimal solutions with efficient convergence rates for different MPLS/GMPLS network scales. The proposed approach has also been applied with two objective functions;the resource provisioning and the traffic load balancing costs. Our simulations and comparative study showed improved results of the proposed algorithm over the well-known optimization algorithms such as standard PSO, Adaptive PSO, Bat and Dolphin algorithm.

关键词： Communication networks optimization exploration problem multi-objective constrained optimization particle swarm optimization swarm intelligence

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Ship weather routing optimization with dynamic constraints based on reliable synchronous roll prediction

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OCEAN ENGINEERING 2018年 150卷 124-137页

作者： Krata, Przemyslaw Szlapczynska, Joanna Gdynia Maritime Univ Dept Ship Operat Fac Nav Gdynia Poland Gdynia Maritime Univ Dept Nav Fac Nav Gdynia Poland

Ship routing process taking into account weather conditions is a constrained multi-objective optimization problem and it should consider various optimization criteria and constraints. Formulation of a stability-related, dynamic route optimization constraint is presented in this paper. One of the key objectives of a cross ocean sailing is finding a compromise between ship safety and economics of operation. This compromise should be taken into account by the planning procedure and proper optimization algorithm. In this research complex stability related phenomena are adopted as the basis for the constraint set formulation in weather routing. Thus, the synchronous roll, parametric resonance, surf riding and broaching-to are considered according to the IMO MSC.1/Circ.1228 guidance. However, the dangerous resonance motion of the ship depends on her natural period of roll and a degree of tuning to the encounter wave period. This natural period strictly depends on the GZ curve shape and, consequently, on the amplitude of roll. In order to properly model the natural period of roll, a new method utilizing equivalent metacentric height is applied and incorporated into the route optimization. Sample calculations of the cross ocean routes are presented and the effect of the dynamic approach to the constraint set is demonstrated and discussed.

关键词： Weather routing multi-objective constrained optimization Ship synchronous rolling Resonance frequency of rolling Equivalent metacentric height Natural period of ship roll

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Chaos Particle Swarm optimization Algorithm for multi-objective constrained optimization Problems

Chaos Particle Swarm Optimization Algorithm for Multi-Object...

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2010 4th International Conference on Intelligent Information Technology Application (IITA2010)

作者： Dekun Tan Department of Computer Science & Technolog,Nanchang Institute of Technology Nanchang,Jiangxi Province,China 330099

In this paper,an improved particle swarm algorithm based on chaos for multi-objective optimization problems is *** algorithm uses external archive to save the current best solutions,and maintains the archive by density distance,the object dynamic crowding is used to measure Pareto solutions' quality and the strategy of eliminating low dynamic crowding solutions is employed to enhance non-dominated solutions *** order to preserve population diversity,the global best is selected randomly from those non-dominated solutions which have bigger density distance in the *** best position is updated by Pareto dominance *** the basic particle swarm optimization gets into local convergence,chaos disturbance is introduced to guide the swarm to escape from local optima,so it can overcome the defect of traditional particle swarm optimization algorithm on getting into local best and enhance the global exploratory capability of *** proposed algorithm is compared with two well known multi-objective evolutionary algorithms through three standard test functions,the numerical experiment results demonstrate that the obtained Pareto optimal solutions by the algorithm can rapidly converge to the Pareto front and uniformly spread along the front.

关键词： multi-objective constrained optimization chaos particle swarm optimization pareto solutions external archive density distance

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A spectral Rayleigh–Ritz scheme for nonlinear partial differential systems of first order

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Journal of the Egyptian Mathematical Society 2016年第3期24卷 373-380页

作者： Hussien S. Hussien Department of Mathematics Faculty of Science South Valley University Qena 83523 Egypt

Nonlinear systems of partial differential problems of first order with Dirichlet boundary conditions is considered. ultraspherical integral zero- boundary (UIZB) method is combined with Rayleigh–Ritz method to approximate the unknowns. The approach converts the problem to be a multi-objective constrained optimization problem which is easier to solve. Accurate results can be obtained by selecting a limited number of collocation points. Numerical examples are included to demonstrate the accuracy and efficiency of the propped method.

关键词： Ultraspherical integral zeros method Rayleigh–Ritz method Boundary value problems Nonlinear first-order partial differential systems multi-objective constrained optimization 33C45 65M60 35F30 90C29 65K10

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A multi-objective problem based on fuzzy inference with application to parametric design of an electrophotographic system

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第7期38卷 8673-8683页

作者： Chen, Cheng-Lun Weng, Ching-Pang Natl Chung Hsing Univ Dept Elect Engn Taichung 40227 Taiwan

This paper proposes a systematic approach to conduct system-level optimal parametric design for a class of electrophotographic systems. A conventional monochrome laser printer serves as the platform for verification of the proposed optimal design approach. Besides performance, we incorporate two other practical indices, i.e., cost and energy consumption, into design objectives and formulate a multi-objective optimization problem. A fuzzy inference system is established to provide the nonlinear or linguistic mapping between the decision variables and the design objectives. For comparative purpose, the problem is solved using both single-objective and multi-objective optimization algorithms. Note that the proposed approach is also applicable to other complex systems. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Fuzzy inference system Genetic algorithm Electrophotographic system multi-objective constrained optimization

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Optimal parameter design for an electrophotographic system using fuzzy multi-objective genetic algorithm

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JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 2011年第3期34卷 367-382页

作者： Weng, Ching-Pang Chen, Cheng-Lun Natl Chung Hsing Univ Dept Elect Engn Taichung 40227 Taiwan

This article proposes a systematic approach to conduct system level optimal parameter design for electrophotographic systems. A conventional monochrome laser printer serves as the platform for verification of the proposed optimal design approach. Besides performance, we incorporate two other practical indices, i.e., cost and energy consumption, into design objectives and formulate a multi-objective optimization problem. Fuzzy logics are employed to provide the nonlinear or linguistic mapping between the decision variables and the design objectives. For comparison, the problem is solved using both single-objective and multi-objective optimization algorithms. Note that the proposed approach is also applicable to other complex systems.

关键词： fuzzy system genetic algorithm electrophotographic system multi-objective constrained optimization

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multi-objective Mean Particle Swarm optimization Algorithm

Multi-Objective Mean Particle Swarm Optimization Algorithm

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Pei, Shengyu Zhou, Yongquan Guangxi Univ Nationalities Coll Math & Comp Sci Nanning Guangxi Peoples R China

ISBN: (纸本)9781424467129

In this paper, Pareto non-dominated ranking, crowding distance, tournament selection methods and mean particle swarm optimization were introduced, we using these concepts, a novel mean particle swarm optimization algorithm for multi-objective optimization problem is proposed. Finally, three standard non-constrained multi-objective functions and four constrained multi-objective functions are used to test the performance of the algorithm. The experiment results show that the proposed approach is an efficient and feasible.

关键词： Particle swarm optimization Mean particle swarm optimization multi-objective constrained optimization Pareto non-dominated Crowding distance

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