Optimisation of new energy vehicle traffic flow and application of hybrid multi-objective evolutionary algorithm based on internet of things simulation of urban mobility simulation platform

This paper deals with the application of a heuristic-based evolutionary algorithms (EAs) on a specific industrial problem - the scheduling of rapid transit systems. The system under consideration is a medium-sized mass rapid transit (MRT) system and the dual objectives of minimizing operating costs and passenger dissatisfaction are considered. Making use of concepts such as Pareto-optimality and multiobjective evolutionary algorithms, the authors applied a multiobjective evolutionary algorithm (MOEA) to solve the problem. Comparison studies were done with current method, obtaining satisfactory results.

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

In the evolutionary computing community, the remarkable language-handling capabilities and reasoning power of large language models (LLMs) have significantly enhanced the functionality of evolutionary algorithms (EAs), enabling them to tackle optimization problems involving structured language or program code. Although this field is still in its early stages, its impressive potential has led to the development of various LLM-based EAs. To effectively evaluate the performance and practical applicability of these LLM-based EAs, benchmarks with real-world relevance are essential. In this paper, we focus on LLM-based recommender systems (RSs) and introduce a benchmark problem set, named RSBench, specifically designed to assess the performance of LLM-based EAs in recommendation prompt optimization. RSBench emphasizes session-based recommendations, aiming to discover a set of Pareto optimal prompts that guide the recommendation process, providing accurate, diverse, and fair recommendations. We develop three LLM-based EAs based on established EA frameworks and experimentally evaluate their performance using RSBench. Our study offers valuable insights into the application of EAs in LLM-based RSs. Additionally, we explore key components that may influence the overall performance of the RS, providing meaningful guidance for future research on the development of LLM-based EAs in RSs.

Application of modern evolutionary algorithm in the intelligent analysis of mining instruments is studied in this paper. Modern digital technology, the automatic control technology, communication technology, information technology, big data technology and the other advanced technologies are increasingly used in the general construction of intelligent mines to realize the coordination of coal mining, sorting processing, transportation, sales and other links. Hence, 2 novelties are proposed. First, intelligent mine platform mainly relies on the Internet of Things coding principle, which standardizes various basic information coding and recognition systems of the mines, and combines mine automation, the IoT framework is used to construct the system. Second, the automated instruments have been then widely used in many industrial production fields such as electric power, chemical industry and petroleum. The intelligent model is used to achieve an efficient analysis of the mentioned question. The basic performance of the model is evaluated through the experiment.

The loss of population diversity is one of the main problems in some applications of evolutionary algorithms. In order to maintain useful population diversity some special techniques must be used, like niching or co-evolutionary mechanisms. In this paper the mechanisms for maintaining population diversity in agent-based multi-objective (co-)evolutionary algorithms are proposed. The presentation of techniques is accompanied by the results of experiments and comparisons to classical evolutionary multi-objective algorithms.

Two primary goals of a multi-objective evolutionary algorithm (MOEA) for solving multi-objective optimization problems are to find as many nondominated solutions as possible toward the true Pareto front and to maintain diversity of Pareto-optimal solutions along the tradeoff curves. However, few MOEAs can achieve these two goals concurrently. This study presents a new hybrid MOEA, the niched Pareto tabu search combined with a genetic algorithm (NPTSGA), in which the global search ability of niched Pareto tabu search (NPTS) is improved by the diversification of candidate solutions that arose from the evolving population of nondominated sorting genetic algorithm-II (NSGA-II). The NPTSGA coupled with a flow and transport model is developed for multi-objective optimal design of groundwater remediation systems. The proposed methodology is then applied to a large field-scale groundwater remediation system for cleanup of large trichloroethylene plume at the Massachusetts Military Reservation in Cape Cod, Massachusetts. Furthermore, a master-slave (MS) parallelization scheme based on the Message Passing Interface is incorporated into the NPTSGA to implement objective function evaluations in a distributed processor environment, which can greatly improve the efficiency of the NPTSGA in finding Pareto-optimal solutions to the real-world applications. This study shows that the MS parallel NPTSGA in comparison with the original NPTS and NSGA-II can balance the tradeoff between the diversity and optimality of solutions during the search process and is an efficient and effective tool for optimizing the multi-objective design of groundwater remediation systems under complicated hydrogeologic conditions.

Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA) can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M) and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M) a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling.

A software package for development, analysis and application of multiobjective evolutionary algorithms is described. The object-oriented design of this kit for evolutionary algorithms (KEA) offers a good suitable environment for various kinds of optimization tasks. It provides an interface to evaluate multi-objective fitness functions written in Java or C/C++ using a variety of multi-objective evolutionary algorithms (MOEA). In addition KEAcontains several state-of-the-art comparison methods for performance measure of algorithms. Furthermore KEAis able to display the progress of optimization in a dynamic display or just to display the results of optimization in a static visualization mode. This paper introduces the main concepts of the KEA-tool. Examples illustrate how to work with it and how to extend its functionality.

Application and benchmarking of multi-objective evolutionary algorithms on high-dose-rate brachytherapy planning for prostate cancer treatment

In the area of bioinformatics, the identification of gene subsets responsible for classifying available samples to two or more classes (for example, classes being 'malignant' or 'benign') is an important task. The main difficulties in solving the resulting optimization problem are the availability of only a few samples compared to the number of genes in the samples and the exorbitantly large search space of solutions. Although there exist a few applications of evolutionary algorithms (EAs) for this task, we treat the problem as a multi-objective optimization problem of minimizing the gene subset size and simultaneous minimizing the number of misclassified samples. Contrary to the past studies, we have discovered that a small gene subset size (such as four or five) is enough to correctly classify 100% or near 100% samples for three cancer samples (Leukemia, Lymphoma, and Colon). Besides a few variants of NSGA-II, in one implementation NSGA-II is modified to find multi-modal non-dominated solutions discovering as many as 630 different three-gene combinations providing a 100% correct classification to the Leukemia data. In order to perform the identification task with more confidence, we have also introduced a threshold in the prediction strength. All simulation results show consistent gene subset identifications on three disease samples and exhibit the flexibilities and efficacies in using a multi-objective EA for the gene identification task.

The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of performance, or objectives, which need to be optimized simultaneously. Under this context, multiobjective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two-part paper, we have made a comprehensive survey on the recent developments of multiobjective evolutionary algorithms for data mining problems. In this paper, Part I, some basic concepts related to multiobjective optimization and data mining are provided. Subsequently, various multiobjective evolutionary approaches for two major data mining tasks, namely feature selection and classification, are surveyed. In Part II of this paper, we have surveyed different multiobjective evolutionary algorithms for clustering, association rule mining, and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain.

Reliable classification of two-class cancer data using evolutionary algorithms

The development of radio frequency identification (RFID) technology generates the most challenging RFID network planning (RNP) problem, which needs to be solved in order to operate the large‐scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP‐hard multi‐objective problem. The application of evolutionary algorithm (EA) and swarm intelligence (SI) for solving multiobjective RNP (MORNP) has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC), the nondominated sorting genetic algorithm II (NSGA‐II), and the multiobjective particle swarm optimization (MOPSO), on MORNP instances of different nature, namely, the two‐objective and three‐objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA‐II and MOPSO in terms of optimization accuracy and computation robustness.

In the real world, it is challenging to determine optimal solutions over multiple conflicting objectives in complex systems. As a mainstream method for solving multi-objective problems, the development and the application of Evolutionary Algorithm (EA) methods have attracted thousands of researches since the 1950s. However, as we know, there are few studies on the comprehensive review of multi-objective EA (MOEA) methods in general domains. In this review research, firstly, the categories of MOEA methods according to the classification strategy of reproduction operators is proposed. Then, a systematic literature search methodology and logical citation management are introduced in order to create a literature pool for further analysis. On the basis of the literature pool, the categories of MOEA methods concerning three aspects and the application domains are analyzed. The purpose of this review is to provide a comprehensive view and a guide reference for the MOEA method selection on solving a specific type of multi-objective optimization problems (MOPs).