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Regularization-free multicriteria optimization of polymer viscoelasticity model

APPLIED SOFT COMPUTING 2022年 124卷

作者： Monaco, Francisco Jose Denysiuk, Roman Delbem, Alexandre Claudio Botazzo Gaspar-Cunha, Antonio Univ Sao Paulo Inst Math & Comp Sci Dept Comp Syst CEP Ave Trabalhador Sao-carlense 400 Caixa Postal 66 BR-13560970 Sao Carlos SP Brazil Univ Minho IPC Inst Polymers & Composites Dept Polymer Engn Campus Azurem P-4800058 Guimaraes Portugal

This paper introduces a multiobjective optimization (MOP) method for nonlinear regression analysis which is capable of simultaneously minimizing the model order and estimating parameter values without the need of exogenous regularization constraints. The method is introduced through a case study in polymer rheology modeling. Prevailing approaches in this field tackle conflicting optimization goals as a monobjective problem by aggregating individual regression errors on each dependent variable into a single weighted scalarization function. In addition, their supporting deterministic numerical methods often rely on assumptions which are extrinsic to the problem, such as regularization constants and restrictions on parameter distribution, thereby introducing methodology inherent biases into the model. Our proposed non-deterministic MOP strategy, on the other hand, aims at finding the Pareto-front of all optimal solutions with respect not only to individual regression errors, but also to the number of parameters needed to fit the data, automatically reducing the model order. The evolutionary computation approach does not require arbitrary constraints on objective weights, regularization parameters or other exogenous assumptions to handle the ill-posed inverse problem. The article discusses the method rationales, implementation, simulation experiments, and comparison with other methods, with experimental evidences that it can outperform state-of-art techniques. While the discussion focuses on the study case, the introduced method is general and immediately applicable to other problem domains. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Multiobjective optimization evolutionary computation Polymer rheology computational modeling

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Real Environment-Aware Multisource Data-Associated Cold Chain Logistics Scheduling: A Multiple Population-Based Multiobjective Ant Colony System Approach

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第12期23卷 23613-23627页

作者： Wu, Li-Jiao Chen, Zong-Gan Chen, Chun-Hua Li, Yun Jeon, Sang-Woon Zhang, Jun Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Pazhou Lab Guangzhou 510330 Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China Univ Elect Sci & Technol China Ind Artificial Intelligence Ctr Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China I4AI Ltd London WC1N 3AX England Hanyang Univ Dept Elect & Commun Engn Ansan 15588 South Korea Zhejiang Normal Univ Jinhua 321004 Zhejiang Peoples R China Hanyang Univ Ansan 15588 South Korea

Cold chain logistics (CCL) scheduling is important for smart cities as it directly affects the service quality and operating profits of logistics companies. However, traditional CCL models seldom reflect the real transportation environment, making the solutions hardly applicable to the real CCL scenes. Hence, this paper attempts to establish a multisource data-associated CCL model oriented to the real transportation environment. This environment is considered by employing the real-captured driving duration and distance between any two places. Three scheduling objectives (namely, quality losses, personnel and vehicle costs, and transportation costs) are taken into account. To efficiently solve the proposed multisource data-associated multiobjective CCL model, a multiple population-based multiobjective ant colony system (MPMOACS) approach is proposed. Based on the multiple populations for multiple objectives framework, the MPMOACS approach can optimize multiple objectives sufficiently, and thus obtain promising solutions distributed along the entire Pareto front. To further enhance the performance of the MPMOACS, a ranking-based local search strategy is also designed. Experiments are conducted on not only the existing benchmark instances but also a real environment-aware multisource dataset that is built based on real-captured transportation data of Guangzhou and Shenzhen, China. Compared with six state-of-the-art and very recent well-performing multiobjective optimization approaches, the proposed MPMOACS approach exhibits the overall best performance.

关键词： Transportation Data models Costs Logistics Optimization Search problems Personnel evolutionary computation multisource data association cold chain logistics scheduling multiobjective optimization ant colony system

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Exploiting multi-level parallel metaheuristics and heterogeneous computing to boost phylogenetics

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2022年 127卷 208-224页

作者： Santander-Jimenez, Sergio Vega-Rodriguez, Miguel A. Sousa, Leonel Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Campus Univ S-N Caceres 10003 Spain Inst Engn Sistemas & Comp Invest & Desenvolvimen Rua Alves Redol 9 P-1000029 Lisbon Portugal

Optimization problems are becoming increasingly difficult challenges as a result of the definition of more realistic formulations and the availability of larger input data. Fortunately, the computing capabilities of state-of-the-art heterogeneous systems represent an opportunity to deal with the main complexity factors of these problems. These platforms open the door to the definition of robust metaheuristic solvers, in which parallel computations of different nature can be efficiently mapped to the most suitable architectures and hardware resources. This work investigates the combination of multi-level parallelism and heterogeneous computing to address an important multiobjective problem in bioinformatics: phylogenetics. A parallel metaheuristic approach, based on the joint exploitation of parallel tasks at the algorithm, iteration, and solution levels, is proposed to tackle computationally intensive inferences on CPU+GPU systems. Different heterogeneous design alternatives are also discussed, in accordance with the way the interactions between CPU and GPU are handled. The experimental evaluation of the proposal on real-world biological datasets points out the benefits of using multi-level, heterogeneous strategies, reporting accelerations up to 396x over the baseline metaheuristic as well as significant energy savings with regard to other parallel approaches, without impacting multiobjective solution quality. (C) 2021 The Author(s). Published by Elsevier B.V.

关键词： Heterogeneous computing Multi-level parallelism evolutionary computation High performance computing Bioinformatics

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Image Deformation Estimation via Multiobjective Optimization

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IEEE ACCESS 2022年 10卷 53307-53323页

作者： Nakane, Takumi Xie, Haoran Zhang, Chao Univ Fukui Dept Engn Fukui 9108507 Japan Japan Adv Inst Sci & Technol Nomi 9231211 Japan

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.

关键词： Strain Estimation Optimization Deformable models computational modeling evolutionary computation Genetic algorithms Genetic algorithm image deformation estimation image registration multi-objective evolutionary algorithm

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Indicator-Based evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems

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IEEE TRANSACTIONS ON evolutionary computation 2022年第2期26卷 379-391页

作者： Yuan, Jiawei Liu, Hai-Lin Ong, Yew-Soon He, Zhaoshui Guangdong Univ Technol Sch Automat Guangzhou 510520 Peoples R China Guangdong Univ Technol Minist Educ Key Lab IoT Intelligent Informat Proc & Syst Inte Guangzhou 510520 Peoples R China Guangdong Univ Technol Sch Appl Math Guangzhou 510520 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

To prevent the population from getting stuck in local areas and then missing the constrained Pareto front fragments in dealing with constrained multiobjective optimization problems (CMOPs), it is important to guide the population to evenly explore the promising areas that are not dominated by all examined feasible solutions. To this end, we first introduce a cost value-based distance into the objective space, and then use this distance and the constraints to define an indicator to evaluate the contribution of each individual to exploring the promising areas. Theoretical studies show that the proposed indicator can effectively guide population to focus on exploring the promising areas without crowding in local areas. Accordingly, we propose a new constraint handling technique (CHT) based on this indicator. To further improve the diversity of population in the promising areas, the proposed indicator-based CHT divides the promising areas into multiple subregions, and then gives priority to removing the individuals with the worst fitness values in the densest subregions. We embed the indicator-based CHT in evolutionary algorithm and propose an indicator-based constrained multiobjective algorithm for solving CMOPs. Numerical experiments on several benchmark suites show the effectiveness of the proposed algorithm. Compared with six state-of-the-art constrained evolutionary multiobjective optimization algorithms, the proposed algorithm performs better in dealing with different types of CMOPs, especially in those problems that the individuals are easy to appear in the local infeasible areas that dominate the constrained Pareto front fragments.

关键词： Statistics Sociology Optimization evolutionary computation Uninterruptible power systems Search problems Constraint handling Constraint handling evolutionary algorithm indicator multiobjective optimization

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Balancing Objective Optimization and Constraint Satisfaction in Constrained evolutionary Multiobjective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9559-9572页

作者： Tian, Ye Zhang, Yajie Su, Yansen Zhang, Xingyi Tan, Kay Chen Jin, Yaochu Anhui Univ Inst Phys Sci & Informat Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Anhui Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Univ Surrey Dept Comp Sci Guildford GU2 7XH Surrey England

Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions.

关键词： Statistics Sociology Optimization evolutionary computation Search problems Convergence Sorting Constrained multiobjective optimization problems (CMOPs) constraint satisfaction evolutionary algorithm objective optimization

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Optimization Techniques for Green Layout Design in Manufacturing Industries: A Meta-Heuristic Analysis

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STROJNISKI VESTNIK-JOURNAL OF MECHANICAL ENGINEERING 2022年第11期68卷 683-692页

作者： Sherfudeen, Sheik Sulaiman Athinamilagi, Muthiah Venkataramanujam, Janakiraman Francis Xavier Engn Coll Dept Mech Engn Tirunelveli India PSR Engn Coll Dept Mech Engn Sivakasi India Vaigai Coll Engn Dept Mech Engn Melur India

Many research papers and much legislation has been published in recent years to control or reduce factory pollution. However, only a few articles have discussed pollution from manufacturing facilities, specifically shop floors, even though this a specific single objective problem. In this research framework, a new variant technique of the jelly fish concept adaptive salp swarm optimization (ASSO) with a familiar Lagrangian relaxation model for lowering Total Material Handling Costs (TMHC) and carbon dioxide (CO2) emissions is presented. Using the Mat Lab software and the improved ASSO, the dragon fly optimization (DFO) algorithm technique, experimental simulations of the existing and recognized design of the studied industry were performed. The simulation results were validated and compared to those of other optimization techniques such as ant bee colony (ABC), simulated annealing (SA), and genetic algorithm (GA). It was determined that the proposed methodology, ASSO, was the most efficient, resulting in 40 % reduction compared to ABC, 38 % DFO, 50 % SA, and 40 % GA in the lowest TMHC, as well as an average 20 % reduction of emission rate in green layout design. These techniques could be combined into a hybrid format for further reduction of the emission rate up to 80 %.

关键词： adaptive salp swarm optimization bi-objective function emission evolutionary computation layout design and total handling costs

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A Two-Stage Deep Neuroevolutionary Technique for Self-Adaptive Speech Enhancement

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IEEE ACCESS 2022年 10卷 5083-5102页

作者： LeBlanc, Ryan Selouani, Sid Ahmed Univ Moncton LARIHS Lab Shippegan NB E8S 1P6 Canada

This paper presents a novel self-adaptive approach for speech enhancement in the context of highly nonstationary noise. A two-stage deep neuroevolutionary technique for speech enhancement is proposed. The first stage is composed of a deep neural network (DNN) method for speech enhancement. Two DNN methods were tested at this stage, namely, both a deep complex convolution recurrent network (DCCRN) and a residual long short-term memory neural network (ResLSTM). The ResLSTM method was combined with a minimum mean-square error method to perform a preliminary enhancement. The ResLSTM network is used as an a priori signal-to-noise ratio (SNR) estimator. The second stage implements a self-adaptive multiband spectral subtraction enhancement method using tuning optimization based on a genetic algorithm. The proposed two-stage technique is evaluated using objective measures of speech quality and intelligibility. The experiments are carried out using the NOIZEUS noisy speech corpus using conditions of real-world stationary, colored, and nonstationary noise sources at multiple SNR levels. These experiments demonstrate the advantage of building a cooperative approach using evolutionary and deep learning-based techniques that are capable of achieving robust speech enhancement in adverse conditions. Indeed, the experimental tests show that the proposed two-stage technique outperformed a baseline implementation using a state-of-the-art deep learning approach by an average 13% and 6% improvement for six noise conditions at a -5 dB and a 0 dB input SNR, respectively.

关键词： Tuning Speech enhancement Training Signal to noise ratio Neural networks Noise cancellation Deep learning Adaptive signal processing artificial neural networks evolutionary computation speech enhancement

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A Dynamic Multiobjective evolutionary Algorithm Based on Decision Variable Classification

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1602-1615页

作者： Liang, Zhengping Wu, Tiancheng Ma, Xiaoliang Zhu, Zexuan Yang, Shengxiang Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Shenzhen Pengcheng Lab Shenzhen 518055 Peoples R China Shenzhen Univ Shenzhen Inst Artificial Intelligence & Robot Soc SZU Branch Shenzhen 518060 Peoples R China De Montfort Univ Ctr Computat Intelligence Sch Comp Sci & Informat Leicester LE1 9BH Leics England

In recent years, dynamic multiobjective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multiobjective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multiobjective evolutionary algorithms. Maintaining a good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a DMOEA based on decision variable classification (DMOEA-DVC) is proposed in this article. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and changes response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. The experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms.

关键词： Sociology Statistics Heuristic algorithms Optimization Convergence evolutionary computation Benchmark testing Decision variable classification dynamic multiobjective evolutionary algorithm (DMOEA) dynamic multiobjective optimization problem (DMOP) multiobjective evolutionary algorithm multiobjective optimization problem (MOP)

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Brushless direct current motor design using a self-adaptive JAYA optimisation algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED computation 2022年第3期20卷 139-149页

作者： Yan, Li Zhang, Chuang Qu, Boyang Yu, Kunjie Yue, Caitong Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou Henan Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou Henan Peoples R China

This paper proposes a self-adaptive JAYA (SAJAYA) to maximise the efficiency of brushless direct current (BLDC) motor by optimising the design parameters. In the proposed SAJAYA, a new hybrid learning strategy is designed to maintain the diversity and avoid premature convergence. Further, a self-adaptive selection mechanism is developed based on the evolutionary state of the individuals, in order to automatically assign the original learning strategy of JAYA or the new hybrid learning strategy to each individual. In this manner, the exploration and exploitation abilities of the SAJAYA are expected to be balanced. In addition, an adaptive weight strategy is introduced to the original JAYA to control the degree of the individuals approaching the best solution and avoiding the worst solution during the different evolution stages. Experimental results show that the proposed SAJAYA shows a superior performance in solving the BLDC motor optimisation problem compared with other well-established algorithms.

关键词： optimisation evolutionary computation JAYA algorithm brushless DC wheel motor

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