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Frames-of-Reference-Based Learning: Overcoming Perceptual Aliasing in Multistep Decision-Making Tasks

IEEE TRANSACTIONS ON evolutionary computation 2022年第1期26卷 174-187页

作者： Siddique, Abubakar Browne, Will N. Grimshaw, Gina M. Victoria Univ Wellington Sch Engn & Comp Sci Evolutionary Computat Res Grp Wellington 6140 New Zealand Queensland Univ Technol Sch Elect Engn & Robot Fac Engn Brisbane Qld 4000 Australia Victoria Univ Wellington Sch Psychol Cognit & Affect Neurosci Lab Wellington 6140 New Zealand

Perceptual aliasing challenges reinforcement learning agents. They struggle to learn stable policies by failing to identify and disambiguate perceptually identical states in the environment that require different actions to reach a goal. As the agent often has only a local frame of reference, it cannot represent the global environment. Frame-of-reference-based learning is a feature of vertebrate intelligence that allows multiple simultaneous representations of an environment at different levels of abstraction. This enables the resolution of patterns that are made up of patterns that are made up of features. The evolutionary computation technique of learning classifier systems has shown promise in learning nested patterns in single-step domains. This work uses the frame-of-reference concept within a learning classifier system to learn stable policies in non-Markov multistep domains. Considering aliased states at a constituent level enables the system to place them appropriately in holistic-level policies. Instead of enumerating a huge search space, evolution computation empowers the novel system to evolve fitter rules and policies. The experimental results show that the novel system effectively solves complex aliasing patterns in non-Markov environments that have been challenging to artificial agents. For example, the novel system utilizes only 6.5, 3.71, and 3.22 steps to resolve Maze10, Littman57, and Woods102, respectively.

关键词： Markov processes Task analysis evolutionary computation Reinforcement learning Navigation Biological information theory Tools Building blocks cognitive neuroscience frame of reference learning classifier systems (LCSs) non-Markov mazes perceptual aliasing

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Adaptive Offspring Generation for evolutionary Large-Scale Multiobjective Optimization

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第2期52卷 786-798页

作者： He, Cheng Cheng, Ran Yazdani, Danial Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Offspring generation plays an important role in evolutionary multiobjective optimization. However, generating promising candidate solutions effectively in high-dimensional spaces is particularly challenging. To address this issue, we propose an adaptive offspring generation method for large-scale multiobjective optimization. First, a preselection strategy is proposed to select a balanced parent population, and then these parent solutions are used to construct direction vectors in the decision spaces for reproducing promising offspring solutions. Specifically, two kinds of direction vectors are adaptively used to generate offspring solutions. The first kind takes advantage of the dominated solutions to generate offspring solutions toward the Pareto optimal set (PS) for convergence enhancement, while the other kind uses those nondominated solutions to spread the solutions over the PS for diversity maintenance. The proposed offspring generation method can be embedded in many existing multiobjective evolutionary algorithms (EAs) for large-scale multiobjective optimization. Experiments are conducted to reveal the mechanism of our proposed adaptive reproduction strategy and validate its effectiveness. Experimental results on some large-scale multiobjective optimization problems have demonstrated the competitive performance of our proposed algorithm in comparison with five state-of-the-art large-scale EAs.

关键词： Sociology Pareto optimization Convergence Maintenance engineering evolutionary computation Adaptive offspring generation evolutionary algorithm (EA) large-scale multiobjective optimization

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Kinematic Optimization of 6DOF Serial Robot Arms by Bio-Inspired Algorithms

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IEEE ACCESS 2022年 10卷 110485-110496页

作者： Galan-Uribe, Ervin Morales-Velazquez, Luis Univ Autonoma Queretaro Fac Ingn Campus San Juan Del Rio San Juan Del Rio 76807 Santiago De Que Mexico

Robotic systems are essential to technological development in the industrial, medical, and aerospace sectors. Nevertheless, their use in different applications requires that the robot have the best possible execution efficiency. For this, a robot with specific optimized characteristics is necessary to impact the performance of the specific task. Different techniques have been used in robot optimization, the most widely used being genetic algorithms (GA) and particle swarm optimization (PSO). However, there are optimization algorithms with high convergence speeds inspired by animal behavior whose application in robot optimization has not been reported. In this work, bio-inspired algorithms Harris hawks optimization (HHO) and Grey wolf optimizer (GWO) are applied to a six-degree-of-freedom (6DOF) robot arm design through kinematic optimization. The lengths of the main robot links are optimized to improve the workspace volume and obtain a better-conditioned robot using the structural length index (SLI) and global condition index (GCI) as objective functions. A comparison is made between the proposed algorithms and GA and PSO regarding convergence speed, computational load, and optimality. Similar behavior has been found for HHO, GWO, and PSO compared to GA for both indexes. For the GCI problem, an average improvement of 14% was found when optimizing an industrial robot arm. Furthermore, multiple runs experiment is performed to test the robustness of the algorithms. The results show that HHO is the best technique to obtain an optimal robot design because it needs less than ten iterations to provide a better result despite its computational load.

关键词： Robots Optimization Indexes Genetic algorithms Manipulators Kinematics Service robots evolutionary computation genetic algorithms grey wolf optimizer Harris hawk optimizer particle swarm optimization robot kinematics

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Roosters Algorithm:A Novel Nature-Inspired Optimization Algorithm

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Computer Systems Science & Engineering 2022年第8期42卷 727-737页

作者： Mashar Gencal Mustafa Oral Ardahan University Ardahan75003Turkey Cukurova University Adana01330Turkey

Some species of females,e.g.,chicken,bird,fish etc.,might mate with more than one *** the mating of these polygamous creatures,there is competition between males as well as among their ***,male reproductive success depends on both male competition and sperm *** by this type of sexual life of roosters with chickens,a novel nature-inspired optimization algorithm called Roosters Algorithm(RA)is *** algorithm was modelled and implemented based on the sexual behavior of roosters.13 well-known benchmark optimization functions and 10 IEEE CEC 2018 test functions are utilized to compare the performance of RA with the performance of well-known algorithms;Standard Genetic Algorithm(SGA),Differential Evolution(DE),Particle Swarm Optimization(PSO),Cuckoo Search(CS)and Grey Wolf Optimizer(GWO).Also,non-parametric statistical tests,Friedman and Wilcoxon Signed Rank Tests,were performed to demonstrate the significance of the *** 20 of the 23 functions that were tested,RA either offered the best results or offered similar results to other compared ***,in this paper,we not only present a novel nature-inspired algorithm,but also offer an alternative method to the well-known algorithms commonly used in the literature,at least as effective as them.

关键词： evolutionary computation meta-heuristics optimization roosters algorithm

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Scale-free network-based differential evolution to solve function optimization and parameter estimation of photovoltaic models

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SWARM AND evolutionary computation 2022年第0期74卷

作者： Yu, Yang Gao, Shangce Zhou, MengChu Wang, Yirui Lei, Zhenyu Zhang, Tengfei Wang, Jiahai Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China Univ Toyama Fac Engn Toyama 9308555 Japan New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Ningbo Univ Fac Elect Engn & Comp Sci Zhejiang 315211 Peoples R China Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Peoples R China

Some recent research reveals that a topological structure in meta-heuristic algorithms can effectively enhance the interaction of population, and thus improve their performances. Inspired by it, we creatively investigate the effectiveness of using a scale-free network in differential evolution algorithm, and propose a scale-free network -based differential evolution method. The novelties of this paper include a scale-free network-based population structure and a new mutation operator designed to fully utilize the neighborhood information provided by a scale-free structure. The elite individuals and population at the latest generation are both employed to guide a global optimization process. In this manner, the proposed algorithm owns balanced exploration and exploitation capabilities to alleviate the drawbacks of premature convergence. Experimental and statistical analyses are performed on the CEC'17 benchmark function suite and the parameter estimation of photovoltaic models. Re-sults demonstrate its superior effectiveness and efficiency in comparison with its competitive peers.

关键词： Differential evolution Scale -free network Population structure evolutionary computation

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Particle Swarm Optimization: A Comprehensive Survey

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IEEE ACCESS 2022年 10卷 10031-10061页

作者： Shami, Tareq M. El-Saleh, Ayman A. Alswaitti, Mohammed Al-Tashi, Qasem Summakieh, Mhd Amen Mirjalili, Seyedali Univ York Dept Elect Engn York YO10 5DD N Yorkshire England ASharqiyah Univ Coll Engn Dept Elect & Commun Engn Ibra 400 Oman Xiamen Univ Malaysia Sch Elect & Comp Engn Dept Informat & Commun Technol Sepang 43900 Malaysia Univ Texas MD Anderson Canc Ctr Dept Imaging Phys Houston TX 77030 USA Univ Albaydha Fac Adm & Comp Sci Albaydha Yemen Multimedia Univ Fac Engn Cyberjaya 63100 Selangor Malaysia Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

关键词： Signal processing algorithms Optimization Particle swarm optimization Feature extraction Topology Birds Statistics Applications of PSO binary PSO evolutionary computation feature selection hybrid algorithms meta-heuristic algorithms particle swarm optimization PSO variants

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Multi-system genetic algorithm for complex system optimization

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SOFT COMPUTING 2022年第19期26卷 10187-10205页

作者： Ma, Haiping Shan, Yu Wang, Jinglin Chen, Xiaolei Yang, Zhile Simon, Dan Shaoxing Univ Dept Elect Engn Shaoxing Zhejiang Peoples R China Univ Nottingham Ningbo China Dept Elect & Elect Engn Ningbo Zhejiang Peoples R China Chongqing Univ Posts & Telecommun Sch Automat Chongqing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Guangdong Peoples R China Cleveland State Univ Dept Elect & Comp Engn Cleveland OH 44115 USA

Complex system optimization is an emerging research topic in the field of evolutionary computation, whose goal is to handle complex systems with multiple coupled subsystems, each including multiple objectives and multiple constraints in real-world applications. This paper proposes a multi-system genetic algorithm (MSGA), stemming from implicit parallelism in population-based search algorithms, to solve multiple coupled subsystems simultaneously in a complex system. The proposed MSGA is composed of within-subsystem evolution and cross-subsystem migration operators. The objective of the former is to optimize each subsystem by appropriate search strategies, and the objective of the latter is to exchange information between multiple subsystems by migration, which is based on the similarity probability of objectives and constraints, and the intersection probability of solutions in different subsystems. During migration across subsystems, three statistical approaches of measuring similarity and three metrics of solution intersection in information theory are used to calculate these probabilities. Performance is tested on a set of multi-subsystem benchmark functions, and the simulation results show that cross-subsystem migration plays the key role for the performance of MSGA. Furthermore, the proposed MSGA is compared with other competitive algorithms, and results show that it is a promising multi-system optimization algorithm. In summary, the contribution of this paper is the introduction of multi-system optimization to the EA community.

关键词： evolutionary computation Multi-system genetic algorithm Complex system optimization Multi-objective optimization Migration

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Prediction of IIoT traffic using a modified whale optimization approach integrated with random forest classifier

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JOURNAL OF SUPERCOMPUTING 2022年第8期78卷 10725-10756页

作者： Ikram, Sumaiya Thaseen Priya, V Anbarasu, B. Cheng, Xiaochun Ghalib, Muhammad Rukunuddin Shankar, Achyut Vellore Inst Technol Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India Middlesex Univ Dept Comp Commun London England Amity Univ Dept Comp Sci Noida Uttar Pradesh India

Several organizations are implementing large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT). Such systems are vulnerable to new threats and intrusions because of the nature of their networks. It is necessary to secure such systems by developing feature selection integrated with robust machine learning models. In this paper, a two-phase IIoT traffic prediction model is built for detecting normal and anomalous IIoT behavior. The system is divided into two phases. In the first phase, multi-objective non-dominated sorting with whale optimization approach (MNSWOA) and ideal point method (IPM) is implemented for feature selection. The key quality attributes are retrieved for effective classification. In the second phase, the feature selection results are sent to the random forest (RF) classifier for traffic prediction. Experimental results on IIoT datasets, namely CTU-13, AWID and Gas-Water show that the proposed technique MNSWOA-IPM-RF obtains enhanced performance in terms of accuracy, true positive rate, true negative rate and area under the ROC curve. Also, comparisons indicate that the proposed MNSWOA-IPM-RF model has superior results than benchmark multi-objective optimization techniques.

关键词： Accuracy Feature selection Multi-objective evolutionary computation Random forest Whale optimization

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Fast Greedy Subset Selection From Large Candidate Solution Sets in evolutionary Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2022年第4期26卷 750-764页

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Subset selection plays an important role in the field of evolutionary multiobjective optimization (EMO). Especially, in an EMO algorithm with an unbounded external archive (UEA), subset selection is an essential post-processing procedure to select a prespecified number of solutions as the final result. In this article, we discuss the efficiency of greedy subset selection for the hypervolume, inverted generational distance (IGD), and IGD plus (IGD+) indicators. Greedy algorithms usually efficiently handle the subset selection. However, when a large number of solutions are given (e.g., subset selection from tens of thousands of solutions in a UEA), they often become time consuming. Our idea is to use the submodular property, which is known for the hypervolume indicator, to improve their efficiency. First, we prove that the IGD and IGD+ indicators are also submodular. Next, based on the submodular property, we propose an efficient greedy inclusion algorithm for each indicator. We demonstrate through computational experiments that the proposed algorithms are much faster than the standard greedy subset selection algorithms. The proposed algorithms also help the research on performance indicators.

关键词： Optimization Greedy algorithms Statistics Sociology Approximation algorithms evolutionary computation Clustering algorithms evolutionary many-objective optimization evolutionary multiobjective optimization (EMO) performance indicators submodularity subset selection

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Theoretical Analysis of Accuracy-Based Fitness on Learning Classifier Systems

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IEEE ACCESS 2022年 10卷 64862-64872页

作者： Sugawara, Rui Nakata, Masaya Yokohama Natl Univ Grad Sch Engn Sci Yokohama Kanagawa 2408501 Japan

Like most evolutionary algorithms, accuracy-based learning classifier systems (XCSs) use a fitness metric to recognize the superiority of rules, under a principle that a higher-quality rule has a higher fitness. However, XCS must learn the fitness values under a reinforcement learning scheme. This introduces uncertainty and asynchrony to the fitness estimation while no theoretical work formally guarantees that such a basic principle would hold. The goal of this paper is to complement this fundamental lack in the reliability of XCS by mathematically analyzing its fitness-update scheme. Our main assumption is that the fitness is updated with an absolute accuracy instead of its relative accuracy to sidestep unpredictable dynamics of XCS. Our theoretical conclusion is that the superiority of rules can be correctly recognized through the accuracy-based fitness under finite update times. We further show that recognizing the superiority among low-quality rules is a costly procedure that increases the number of necessary rule-trainings. This drawback indicates that XCS may struggle to identify good parent rules at early generations, degrading the efficiency of evolutionary propagation.

关键词： Estimation Uncertainty Task analysis Genetic algorithms evolutionary computation Statistics Standards Accuracy-based fitness learning classifier system theoretical analysis

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