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Symmetry
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Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry
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Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 December 2024 | Viewed by 6122

Special Issue Editors

Dr. Yirui Wang

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
Interests: evolutionary computation; search problems; chaos; neuralnets; optimisation; backpropagation; covariance matrices
Prof. Dr. Shangce Gao

E-Mail Website
Guest Editor
Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Interests: artificial intelligence; computational intelligence; neural networks
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Yu

E-Mail Website
Guest Editor
College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Interests: evolutionary computation; neural nets; search problems

Special Issue Information

Dear Colleagues,

Computational intelligence is an important branch of artificial intelligence. Nowadays, evolutionary computation as a part of computational intelligence is widely used to solve various numerical problems and real-world engineering problems. Its application and development bring a great contribution to the optimization domain. Thus, it is of great interest to investigate the role and significance of evolutionary computation, metaheuristics, and nature-inspired algorithms in optimizing distinctive problems such as model symmetry/asymmetry, model architecture and hyperparameters, numerical functions, and industrial processing.

This Special Issue aims to bring together both experts and newcomers from either academia or industry to discuss new and existing issues concerning evolutionary computation and optimization. The research topics include single-objective optimization, multi-objective optimization, combinatorial optimization, and real-world problems, as well as industrial control, job-shop scheduling, pattern recognition, and computer vision. There is no limit on the number of pages, but the submissions must demonstrate an understanding of the theme and contribute to the specified topic.

Dr. Yirui Wang
Dr. Shangce Gao
Dr. Yang Yu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evolutionary computation
  • metaheuristics
  • nature-inspired algorithms
  • single-objective optimization
  • multi-objective optimization
  • real-world engineering application
  • intelligent systems
  • Artificial Intelligence

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Published Papers (5 papers)

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Research

18 pages, 1667 KiB  
Article
An Improved NSGA-III with a Comprehensive Adaptive Penalty Scheme for Many-Objective Optimization
by Xinghang Xu, Du Cheng, Dan Wang, Qingliang Li and Fanhua Yu
Symmetry 2024, 16(10), 1289; https://doi.org/10.3390/sym16101289 - 1 Oct 2024
Cited by 1 | Viewed by 998
Abstract
Pareto dominance-based algorithms face a significant challenge in handling many-objective optimization problems. As the number of objectives increases, the sharp rise in non-dominated individuals makes it challenging for the algorithm to differentiate their quality, resulting in a loss of selection pressure. The application [...] Read more.
Pareto dominance-based algorithms face a significant challenge in handling many-objective optimization problems. As the number of objectives increases, the sharp rise in non-dominated individuals makes it challenging for the algorithm to differentiate their quality, resulting in a loss of selection pressure. The application of the penalty-based boundary intersection (PBI) method can balance convergence and diversity in algorithms. The PBI method guides the evolution of individuals by integrating the parallel and perpendicular distances between individuals and reference vectors, where the penalty factor is crucial for balancing these two distances and significantly affects algorithm performance. Therefore, a comprehensive adaptive penalty scheme was proposed and applied to NSGA-III, named caps-NSGA-III, to achieve balance and symmetry between convergence and diversity. Initially, each reference vector’s penalty factor is computed based on its own characteristic. Then, during the iteration process, the penalty factor is adaptively adjusted according to the evolutionary state of the individuals associated with the corresponding reference vector. Finally, a monitoring strategy is designed to oversee the penalty factor, ensuring that adaptive adjustments align with the algorithm’s needs at different stages. Through a comparison involving benchmark experiments and two real-world problems, the competitiveness of caps-NSGA-III was demonstrated. Full article
(This article belongs to the Special Issue Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry)
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19 pages, 717 KiB  
Article
Imperative Genetic Programming
by Iztok Fajfar, Žiga Rojec, Árpád Bűrmen, Matevž Kunaver, Tadej Tuma, Sašo Tomažič and Janez Puhan
Symmetry 2024, 16(9), 1146; https://doi.org/10.3390/sym16091146 - 3 Sep 2024
Viewed by 841
Abstract
Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like [...] Read more.
Genetic programming (GP) has a long-standing tradition in the evolution of computer programs, predominantly utilizing tree and linear paradigms, each with distinct advantages and limitations. Despite the rapid growth of the GP field, there have been disproportionately few attempts to evolve ’real’ Turing-like imperative programs (as contrasted with functional programming) from the ground up. Existing research focuses mainly on specific special cases where the structure of the solution is partly known. This paper explores the potential of integrating tree and linear GP paradigms to develop an encoding scheme that universally supports genetic operators without constraints and consistently generates syntactically correct Python programs from scratch. By blending the symmetrical structure of tree-based representations with the inherent asymmetry of linear sequences, we created a versatile environment for program evolution. Our approach was rigorously tested on 35 problems characterized by varying Halstead complexity metrics, to delineate the approach’s boundaries. While expected brute-force program solutions were observed, our method yielded more sophisticated strategies, such as optimizing a program by restricting the division trials to the values up to the square root of the number when counting its proper divisors. Despite the recent groundbreaking advancements in large language models, we assert that the GP field warrants continued research. GP embodies a fundamentally different computational paradigm, crucial for advancing our understanding of natural evolutionary processes. Full article
(This article belongs to the Special Issue Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry)
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16 pages, 10391 KiB  
Article
Enhancing NSGA-II Algorithm through Hybrid Strategy for Optimizing Maize Water and Fertilizer Irrigation Simulation
by Jinyang Du, Renyun Liu, Du Cheng, Xu Wang, Tong Zhang and Fanhua Yu
Symmetry 2024, 16(8), 1062; https://doi.org/10.3390/sym16081062 - 17 Aug 2024
Viewed by 1297
Abstract
In optimization problems, the principle of symmetry provides important guidance. This article introduces an enhanced NSGA-II algorithm, termed NDE-NSGA-II, designed for addressing multi-objective optimization problems. The approach employs Tent mapping for population initialization, thereby augmenting its search capability. During the offspring generation process, [...] Read more.
In optimization problems, the principle of symmetry provides important guidance. This article introduces an enhanced NSGA-II algorithm, termed NDE-NSGA-II, designed for addressing multi-objective optimization problems. The approach employs Tent mapping for population initialization, thereby augmenting its search capability. During the offspring generation process, a hybrid local search strategy is implemented to augment the population’s exploration capabilities. It is crucial to highlight that in elite selection, norm selection and average distance elimination strategies are adopted to strengthen the selection mechanism of the population. This not only enhances diversity but also ensures convergence, thereby improving overall performance. The effectiveness of the proposed NDE-NSGA-II is comprehensively evaluated across various benchmark functions with distinct true Pareto frontier shapes. The results consistently demonstrate that the NDE-NSGA-II method presented in this paper surpasses the performance metrics of the other five methods. Lastly, the algorithm is integrated with the DSSAT model to optimize maize irrigation and fertilization scheduling, confirming the effectiveness of the improved algorithm. Full article
(This article belongs to the Special Issue Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry)
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25 pages, 753 KiB  
Article
Multi-Task Multi-Objective Evolutionary Search Based on Deep Reinforcement Learning for Multi-Objective Vehicle Routing Problems with Time Windows
by Jianjun Deng, Junjie Wang, Xiaojun Wang, Yiqiao Cai and Peizhong Liu
Symmetry 2024, 16(8), 1030; https://doi.org/10.3390/sym16081030 - 12 Aug 2024
Viewed by 1205
Abstract
The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, [...] Read more.
The vehicle routing problem with time windows (VRPTW) is a widely studied combinatorial optimization problem in supply chains and logistics within the last decade. Recent research has explored the potential of deep reinforcement learning (DRL) as a promising solution for the VRPTW. However, the challenge of addressing the VRPTW with many conflicting objectives (MOVRPTW) still remains for DRL. The MOVRPTW considers five conflicting objectives simultaneously: minimizing the number of vehicles required, the total travel distance, the travel time of the longest route, the total waiting time for early arrivals, and the total delay time for late arrivals. To tackle the MOVRPTW, this study introduces the MTMO/DRP-AT, a multi-task multi-objective evolutionary search algorithm, by making full use of both DRL and the multitasking mechanism. In the MTMO/DRL-AT, a two-objective MOVRPTW is constructed as an assisted task, with the objectives being to minimize the total travel distance and the travel time of the longest route. Both the main task and the assisted task are simultaneously solved in a multitasking scenario. Each task is decomposed into scalar optimization subproblems, which are then solved by an attention model trained using DRL. The outputs of these trained models serve as the initial solutions for the MTMO/DRL-AT. Subsequently, the proposed algorithm incorporates knowledge transfer and multiple local search operators to further enhance the quality of these promising solutions. The simulation results on real-world benchmarks highlight the superior performance of the MTMO/DRL-AT compared to several other algorithms in solving the MOVRPTW. Full article
(This article belongs to the Special Issue Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry)
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15 pages, 3489 KiB  
Article
Short-Term Electrical Load Forecasting Using an Enhanced Extreme Learning Machine Based on the Improved Dwarf Mongoose Optimization Algorithm
by Haocheng Wang, Yu Zhang and Lixin Mu
Symmetry 2024, 16(5), 628; https://doi.org/10.3390/sym16050628 - 18 May 2024
Cited by 1 | Viewed by 1010
Abstract
Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an [...] Read more.
Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy. Full article
(This article belongs to the Special Issue Evolutionary Computation, Metaheuristics, Nature-Inspired Algorithms, and Symmetry)
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