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Multi-objective social group optimization for machining process

evolutionary INTELLIGENCE 2024年第3期17卷 1655-1676页

作者： Naik, Anima Vignans Inst Informat Technol Dept CSE Visakhapatnam 530049 Andhrapradesh India

A multi-objective social group optimization (MOSGO) is proposed in this study as a method for resolving problems with multiple objectives. The initial step is to use an external chronicle with a predetermined size to store the existing non-dominated Pareto optimum solutions. Following that, solutions are chosen from this repository using a roulette wheel mechanism based on the coverage of solutions to guide individuals in the direction of promising regions of multi-objective search spaces. The proposed method is tested on ten CEC2009 multi-objective benchmark problems and it has been compared to two well-known meta-heuristics: the multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm and the multi-objective particle swarm optimization (MOPSO) algorithm. It is essential to the machining processes to choose the optimum machining parameters in order to guarantee product quality, lower machining costs, boost productivity, and preserve resources for sustainability. Hence, MOSGO, a posterior multi-objective optimization method, is used in this study to tackle the multi-objective optimization problems of three machining processes, including turning, wire-electric-discharge machining, and laser cutting. The results of the MOSGO algorithm are compared with the results obtained using GA, NSGA-II, PSO, and iterative search methods and are found to be comparable. Here it has been observed that the MOSGO algorithm achieved the Pareto-optimal set of solutions in a very less number of function evaluations as compared to other algorithms showing a higher convergence speed. The Pareto-optimal set of solutions for each optimization problem is obtained and reported. These Pareto-optimal sets of solutions are helpful for real production systems and will aid the decision-maker in tumultuous situations.

关键词： Multi-objective optimization evolutionary algorithms Machining process Pareto optimality MOSGO Benchmark functions

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Design of Content Distribution Networks for Smart Cities 7th

Design of Content Distribution Networks for Smart Cities

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7th Iberoamerican Congress on Smart Cities-ICSC CITIES

作者： Goni, Gerardo Nesmachnow, Sergio Chernykh, Andrei Univ Republica Montevideo Uruguay CICESE Res Ctr Ensenada Baja California Mexico

ISBN: (纸本)9783031853234;9783031853241

This article presents an approach applying a multiobjective evolutionary approach for the problem of designing Content Distribution Networks in the context of smart cities. The problem at had is NP-hard problem, thus efficient alternatives to exact methods are needed to solve the problem in reasonable execution times. A specific multiobjective evolutionary algorithm is proposed to solve the problem, using ad-hoc solutions representations and operators to optimize system- and user-related metrics. The main results over real problem instances show that the solutions computed the proposed multiobjective evolutionary algorithm are highly competitive in both cost and quality of service when compared with solutions found using an exact method. The computed solutions demanded significantly less time to be found and provide high diversity, accounting for different trade-offs between the problem objectives.

关键词： Content Distribution Networks evolutionary algorithms computational intelligence location smart cities

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A Multi Objective evolutionary Algorithm based on Decomposition for a Flow Shop Scheduling Problem in the Context of Industry 4.0

INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGE...

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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2022年第4期7卷 433-454页

作者： Rossit, Diego Gabriel Nesmachnow, Sergio Rossit, Daniel Alejandro Univ Nacl Sur Dept Engn Bahia Blanca Buenos Aires Argentina Univ Nacl Sur INMABB Bahia Blanca Buenos Aires Argentina Consejo Nacl Invest Cient & Tecn Bahia Blanca Buenos Aires Argentina Univ Republica Montevideo Uruguay

Under the novel paradigm of Industry 4.0, missing operations have arisen as a result of the increasingly customization of the industrial products in which customers have an extended control over the characteristics of the final products. As a result, this has completely modified the scheduling and planning management of jobs in modern factories. As a contribution in this area, this article presents a multi objective evolutionary approach based on decomposition for efficiently addressing the multi objective flow shop problem with missing operations, a relevant problem in modern industry. Tests performed over a representative set of instances show the competitiveness of the proposed approach when compared with other baseline metaheuristics.

关键词： Industry 4.0 Flow shop Missing operation evolutionary algorithms Multi objective optimization Makespan Total tardiness

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Creating deep neural networks for text classification tasks using grammar genetic programming

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APPLIED SOFT COMPUTING 2023年 135卷

作者： Magalhaes, Dimmy Lima, Ricardo H. R. Pozo, Aurora Univ Fed Parana Dept Informat Curitiba Parana Brazil

Text classification is one of the Natural Language Processing (NLP) tasks. Its objective is to label textual elements, such as phrases, queries, paragraphs, and documents. In NLP, several approaches have achieved promising results regarding this task. Deep Learning-based approaches have been widely used in this context, with deep neural networks (DNNs) adding the ability to generate a representation for the data and a learning model. The increasing scale and complexity of DNN architectures was expected, creating new challenges to design and configure the models. In this paper, we present a study on the application of a grammar-based evolutionary approach to the design of DNNs, using models based on Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Graph Neural Networks (GNNs). We propose different grammars, which were defined to capture the features of each type of network, also proposing some combinations, verifying their impact on the produced designs and performance of the generated models. We create a grammar that is able to generate different networks specialized on text classification, by modification of Grammatical Evolution (GE), and it is composed of three main components: the grammar, mapping, and search engine. Our results offer promising future research directions as they show that the projected architectures have a performance comparable to that of their counterparts but can still be further improved. We were able to improve the results of a manually structured neural network in 8,18% in the best case. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Text classification evolutionary algorithms Genetic programming Automatic design Grammatical evolution Deep neural networks

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Computational generation of a spatial layout through syntactical evaluation and multi-objective evolutionary optimization

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INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING 2022年第3期20卷 610-629页

作者： Cicek, Selen Turhan, Gozde Damla Izmir Econ Univ Sakarya Cd 156 TR-35330 Izmir Turkey

The space layout problem encompasses challenges that rely on a diverse range of contexts regarding urban planning and architectural design, during the traditional design phases which require immense effort and time for the evaluation of the spatial elements' characteristic needs. In order to eliminate the burden of considering all multidimensional design aspects at the same time, this research presents a three-bodied computational method for locating the spaces of the given architectural design program in a project site, according to the defined list of design objectives and criteria. Besides the determination of the layout according to the requirements of the spatial elements, this research proposes an integration of the space syntax theory's analytical compounds in terms of Justified Graph Analysis and Integration Values as the fitness criteria for the multi-objective evolutionary optimization in the computational model. To satisfy the integrity levels of each various characterized element within site organization, that are implied inherently by the architectural design program and generate a sustainable space network layout for the project site.

关键词： computational space layout space syntax spatial organization spatial network evolutionary algorithms

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An enhanced multi-operator differential evolution algorithm for tackling knapsack optimization problem

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NEURAL COMPUTING & APPLICATIONS 2023年第18期35卷 13359-13386页

作者： Sallam, Karam M. Abohany, Amr A. Rizk-Allahi, Rizk M. Univ Canberra Fac Sci & Technol Sch IT & Syst Bruce ACT 2601 Australia Kafrelsheikh Univ Fac Comp & Informat Kafr El Shaikh Egypt Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm 32511 Egypt

The knapsack problem (KP) is a discrete combinatorial optimization problem that has different utilities in many fields. It is described as a non-polynomial time (NP) problem and has several applications in many fields. The differential evolution (DE) algorithm has been successful in solving continuous optimization problems, but it needs further work to solve discrete and binary optimization problems and avoid local optima. According to the literature, no DE search operator or algorithm is optimal for all optimization tasks. As a result, using more than one search operator in a single algorithm architecture, called multi-operator-based algorithms, is a solution to address this problem. These methods outperformed single-based methods for continuous optimization problems. Thus, in this paper, a binary multi-operator differential evolution (BMODE) approach is presented to tackle the 0-1 KP. The presented methodology utilizes multiple differential evolution (DE) mutation strategies with complementary characteristics, with the best mutation operator being asserted utilizing the produced solutions' quality and the population's diversity. In BMODE, two types of transfer functions (TFs) (S-shaped and V-shaped) are used to transfer the continuous solutions to binary ones to be able to calculate the fitness function value. To handle the capacity constraints, a feasibility rule is utilized and some of the infeasible solutions are repaired. The performance of BMODE is tested by solving 40 instances with multiple dimensions, i.e., low, medium, and high. Experimental results of the proposed BMODE are compared with well-known state-of-the-art 0-1 knapsack algorithms. Based on Wilcoxon's nonparametric statistical test (alpha=0.05), the proposed BMODE can obtain the best results against the rival algorithms in most cases, and can work well on stability and computational accuracy.

关键词： evolutionary algorithms Differential evolution Multi-operator algorithms Binary optimization Knapsack problem

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Multiobjective Differential Evolution With Speciation for Constrained Multimodal Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2023年第4期27卷 1115-1129页

作者： Liang, Jing Lin, Hongyu Yue, Caitong Yu, Kunjie Guo, Ying Qiao, Kangjia Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China

This article proposes a novel differential evolution algorithm for solving constrained multimodal multiobjective optimization problems (CMMOPs), which may have multiple feasible Pareto-optimal solutions with identical objective vectors. In CMMOPs, due to the coexistence of multimodality and constraints, it is difficult for current algorithms to perform well in both objective and decision spaces. The proposed algorithm uses the speciation mechanism to induce niches preserving more feasible Pareto-optimal solutions and adopts an improved environment selection criterion to enhance diversity. The algorithm can not only obtain feasible solutions but also retain more well-distributed feasible Pareto-optimal solutions. Moreover, a set of constrained multimodal multiobjective test functions is developed. All these test functions have multimodal characteristics and contain multiple constraints. Meanwhile, this article proposes a new indicator, which comprehensively considers the feasibility, convergence, and diversity of a solution set. The effectiveness of the proposed method is verified by comparing with the state-of-the-art algorithms on both test functions and real-world location-selection problem.

关键词： Benchmark functions constraints evolutionary algorithms multimodal multiobjective speciation

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Grouping promotes both partnership and rivalry with long memory in direct reciprocity

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PLOS COMPUTATIONAL BIOLOGY 2023年第6期19卷 e1011228-e1011228页

作者： Murase, Yohsuke Baek, Seung Ki RIKEN Ctr Computat Sci Kobe Japan Max Planck Inst Evolutionary Biol Max Planck Res Grp Dynam Social Behav Plon Germany Pukyong Natl Univ Dept Sci Comp Busan South Korea

Author summaryIn the evolution of cooperation, to what extent is cognitive capacity essential? The social brain hypothesis argued that the brain size of primates has increased with the social group size to manage complex social interactions, e.g., to reciprocate cooperation and punish free riders. On the other hand, in the study of the repeated Prisoner's Dilemma, it has been shown that simple strategies that remember only the previous round can unilaterally control the payoffs even against more sophisticated strategies having longer memories. Thus, it is not straightforward to answer the question of how the evolution of cooperation changes when players are accessible to more elaborate memory-demanding strategies. This paper studies this question through evolutionary simulations and found that longer memory strategies substantially change the picture when the population has an internal structure. This study thus suggests the joint impact between cognitive capacity and the population structure in the evolution of cooperation, although these have often been studied independently. Biological and social scientists have long been interested in understanding how to reconcile individual and collective interests in the iterated Prisoner's Dilemma. Many effective strategies have been proposed, and they are often categorized into one of two classes, 'partners' and 'rivals.' More recently, another class, 'friendly rivals,' has been identified in longer-memory strategy spaces. Friendly rivals qualify as both partners and rivals: They fully cooperate with themselves, like partners, but never allow their co-players to earn higher payoffs, like rivals. Although they have appealing theoretical properties, it is unclear whether they would emerge in an evolving population because most previous works focus on the memory-one strategy space, where no friendly rival strategy exists. To investigate this issue, we have conducted evolutionary simulations in well-mixed and group-structured p

关键词： Game theory Memory Monte Carlo method Games Markov models Prisoner's dilemma evolutionary algorithms Schematic diagrams

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Remote sensing-based precipitation forecasting using cloud optical characteristics: threshold optimization and evaluation in Northern and Western Iran

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NATURAL HAZARDS 2024年第4期120卷 3661-3675页

作者： Salahi, Ali Ashrafzadeh, Afshin Vazifedoust, Majid Univ Guilan Fac Agr Sci Dept Water Engn Rasht Iran

Historically, certain precipitation events within Iran's northern and western regions have caused severe and potentially catastrophic flood occurrences. The primary focus of this study was to develop an expeditious method for forecasting precipitation occurrence within discrete one-kilometer grid cells, exclusively through the examination of relevant cloud optical characteristics. To establish threshold values associated with cloud optical characteristics, we incorporated data from a significant precipitation event on October 12, 2019, within the studied regions, including IMERG precipitation data and cloud data from the High Rate SEVIRI Level 1.5 Image Data and the Optimal Cloud Analysis. The optimization of these cloud thresholds was accomplished using the NSGA-II algorithm, which aimed to maximize the probability of detection (POD) of precipitation while minimizing the false alarm rate (FAR) across 77,674 pixels located within the study area. The threshold values derived from the October 12, 2019, event were subsequently applied to forecast precipitation in two additional events on October 5, 2018, and March 24, 2019. The results indicated that, for these two events, the probability of correctly identifying pixels with precipitation ranged from 66.9 to 96.1% for the first event and 27.5 to 72.2% for the second event within different three-hour intervals. Across the entire period of precipitation events, the POD and FAR values for the first event were 90.5% and 45.8%, respectively, while for the second event, they were 64.2% and 9.5%. This research provides insights into applying remote sensing data and an advanced algorithm to analyze precipitation events. The optimization of cloud parameter thresholds, as demonstrated at the October 12, 2019, event, holds significant promise for enhancing the accuracy of precipitation forecasting. The results from the subsequent events underscore this approach's potential, showing varying success levels in identifying precipitat

关键词： evolutionary algorithms Flood forecasting Satellite precipitation

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algorithms for path optimizations: a short survey

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COMPUTING 2023年第2期105卷 293-319页

作者： De Sirisuriya, S. C. M. S. Fernando, T. G., I Ariyaratne, M. K. A. Gen Sir John Kotelawala Def Univ Fac Comp Dept Comp Sci Rathmalana Sri Lanka Univ Sri Jayewardenepura Fac Grad Studies Nugegoda Sri Lanka Univ Sri Jayewardenepura Fac Appl Sci Dept Comp Sci Nugegoda Sri Lanka

Path finding is used to solve the problem of finding a traversable path through an environment with obstacles. This problem can be seen in many different fields of study and these areas rely on fast and efficient path finding algorithms. This paper aims to describe and review state of the art optimization techniques that are used on optimized path finding and compare their performances. Moreover, a special attention is paid on the proposed approaches to identify how they are tested on different test cases;whether the test cases are automatically generated or benchmark instances. The review opens avenues about the importance of automatic test case generation to test the different path finding algorithms.

关键词： Path Optimization evolutionary algorithms Swarm Intelligence algorithms Machine Learning algorithms Robotic Path Planing Vehicle Routing

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