Research

12 pages, 283 KiB

Open AccessArticle

A Novel Attribute Reduction Algorithm for Incomplete Information Systems Based on a Binary Similarity Matrix

by Yan Zhou and Yan-Ling Bao

Symmetry 2023, 15(3), 674; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15030674 - 07 Mar 2023

Cited by 2 | Viewed by 793

With databases growing at an unrelenting rate, it may be difficult and complex to extract statistics by accessing all of the data in many practical problems. Attribute reduction, as an effective method to remove redundant attributes from massive data, has demonstrated its remarkable [...] Read more.

With databases growing at an unrelenting rate, it may be difficult and complex to extract statistics by accessing all of the data in many practical problems. Attribute reduction, as an effective method to remove redundant attributes from massive data, has demonstrated its remarkable capability in simplifying information systems. In this paper, we concentrate on reducing attributes in incomplete information systems. We introduce a novel definition of a binary similarity matrix and present a method to calculate the significance of attributes in correspondence. Secondly, We develop a heuristic attribute reduction algorithm using a binary similarity matrix and attribute significance as heuristic knowledge. In addition, we use a numerical example to showcase the practicality and accuracy of the algorithm. In conclusion, we demonstrate through comparative analysis that our algorithm outperforms some existing attribute reduction methods. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

16 pages, 2508 KiB

Open AccessArticle

Effects of Symmetry Restriction on the Antenna Gain Optimized Using Genetic Algorithms

by Michael Renzler, Dominik Mair, Markus Hesche and Thomas Ussmueller

Symmetry 2023, 15(3), 658; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15030658 - 06 Mar 2023

Viewed by 1184

Abstract

Automated design techniques for antennas are highly interesting regarding their ability to cut down on development time and their overall importance for communication technology. Sub-dividing a given area into individual pixels renders methods such as genetic algorithms possible, which enables an automated optimization [...] Read more.

Automated design techniques for antennas are highly interesting regarding their ability to cut down on development time and their overall importance for communication technology. Sub-dividing a given area into individual pixels renders methods such as genetic algorithms possible, which enables an automated optimization process by evolutionary methods. While there are many examples in the literature that use this approach, most of them use the reflection coefficient as the optimization goal. Although this yields satisfying antennas for a variety of different applications, this approach generally does not achieve high directivities or antenna gains. In this work, we present the evolutionary optimization of antenna gain for pixelated antennas and the effect of symmetry restriction on the optimized topologies. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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18 pages, 1509 KiB

Open AccessArticle

A Hybrid Algorithm for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem with Time Windows

by Zakir Hussain Ahmed and Majid Yousefikhoshbakht

Symmetry 2023, 15(2), 486; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15020486 - 12 Feb 2023

Cited by 1 | Viewed by 1327

Abstract

Nowadays, using a rental fleet to transport goods for delivering to customers at a particular time frame is very important in the services and industry. That is why, in this study, we consider the heterogeneous fixed fleet open vehicle routing problem with time [...] Read more.

Nowadays, using a rental fleet to transport goods for delivering to customers at a particular time frame is very important in the services and industry. That is why, in this study, we consider the heterogeneous fixed fleet open vehicle routing problem with time windows, which is one version of the vehicle routing problem with time windows. The problem has not attracted attention so much in the operational research literature than the usual vehicle routing problem. The problem consists of determining the minimum cost routes for a fleet of a fixed number of vehicles with various capacities in order to fulfil the demands of the customer population. Moreover, the vehicles start at the headquarters and terminate at one of the customers. In this study, we introduce a mixed integer programming model and then integrate an exact algorithm to solve this model. Furthermore, a hybrid algorithm (HA) based on modified rank-based ant system is developed and then its efficiency is compared with the exact method and some metaheuristic methods on some standard instances in literature. The results proved the effectiveness of our proposed HA. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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39 pages, 1361 KiB

Open AccessArticle

A Tent Lévy Flying Sparrow Search Algorithm for Wrapper-Based Feature Selection: A COVID-19 Case Study

by Qinwen Yang, Yuelin Gao and Yanjie Song

Symmetry 2023, 15(2), 316; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15020316 - 22 Jan 2023

Cited by 5 | Viewed by 1197

Abstract

The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big [...] Read more.

The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population’s poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent Lévy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms’ performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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22 pages, 5888 KiB

Open AccessArticle

Isokinetic Rehabilitation Trajectory Planning of an Upper Extremity Exoskeleton Rehabilitation Robot Based on a Multistrategy Improved Whale Optimization Algorithm

by Fumin Guo, Hua Zhang, Yilu Xu, Genliang Xiong and Cheng Zeng

Symmetry 2023, 15(1), 232; https://0-doi-org.brum.beds.ac.uk/10.3390/sym15010232 - 13 Jan 2023

Cited by 2 | Viewed by 1210

Abstract

Upper extremity exoskeleton rehabilitation robots have become a significant piece of rehabilitation equipment, and planning their motion trajectories is essential in patient rehabilitation. In this paper, a multistrategy improved whale optimization algorithm (MWOA) is proposed for trajectory planning of upper extremity exoskeleton rehabilitation [...] Read more.

Upper extremity exoskeleton rehabilitation robots have become a significant piece of rehabilitation equipment, and planning their motion trajectories is essential in patient rehabilitation. In this paper, a multistrategy improved whale optimization algorithm (MWOA) is proposed for trajectory planning of upper extremity exoskeleton rehabilitation robots with emphasis on isokinetic rehabilitation. First, a piecewise polynomial was used to construct a rough trajectory. To make the trajectory conform to human-like movement, a whale optimization algorithm (WOA) was employed to generate a bounded jerk trajectory with the minimum running time as the objective. The search performance of the WOA under complex constraints, including the search capability of trajectory planning symmetry, was improved by the following strategies: a dual-population search, including a new communication mechanism to prevent falling into the local optimum; a mutation centroid opposition-based learning, to improve the diversity of the population; and an adaptive inertia weight, to balance exploration and exploitation. Simulation analysis showed that the MWOA generated a trajectory with a shorter run-time and better symmetry and robustness than the WOA. Finally, a pilot rehabilitation session on a healthy volunteer using an upper extremity exoskeleton rehabilitation robot was completed safely and smoothly along the trajectory planned by the MWOA. The proposed algorithm thus provides a feasible scheme for isokinetic rehabilitation trajectory planning of upper extremity exoskeleton rehabilitation robots. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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21 pages, 17306 KiB

Open AccessArticle

An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Angle Preference

by Qing-Hua Ling, Zhi-Hao Tang, Gan Huang and Fei Han

Symmetry 2022, 14(12), 2619; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14122619 - 10 Dec 2022

Viewed by 1299

Abstract

Multi-objective particle swarm optimization (MOPSO) algorithms based on angle preference provide a set of preferred solutions by incorporating a user’s preference. However, since the search mechanism is stochastic and asymmetric, traditional MOPSO based on angle preference are still easy to fall into local [...] Read more.

Multi-objective particle swarm optimization (MOPSO) algorithms based on angle preference provide a set of preferred solutions by incorporating a user’s preference. However, since the search mechanism is stochastic and asymmetric, traditional MOPSO based on angle preference are still easy to fall into local optima and lack enough selection pressure on excellent individuals. In this paper, an improved MOPSO algorithm based on angle preference called IAPMOPSO is proposed to alleviate those problems. First, to create a stricter partial order among the non-dominated solutions, reference vectors are established in the preference region, and the adaptive penalty-based boundary intersection (PBI) value is used to update the external archive. Second, to effectively alleviate the swarm to fall into local optima, an adaptive preference angle is designed to increase the diversity of the population. Third, neighborhood individuals are selected for each particle to update the individual optimum to increase the information exchange among the particles. With the proposed angle preference-based external archive update strategy, solutions with a smaller PBI are given higher priority to be selected, and thus the selection pressure on excellent individuals is enhanced. In terms of an increase in the diversity of the population, the adaptive preference angle adjustment strategy that gradually narrows the preferred area, and the individual optimum update strategy which updates the individual optimum according to the information of neighborhood individuals, are presented. The experimental results on the benchmark test functions and GEM data verify the effectiveness and efficiency of the proposed method. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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20 pages, 5711 KiB

Open AccessArticle

Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm

by Xiaoling Meng and Xijing Zhu

Symmetry 2022, 14(9), 1843; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14091843 - 05 Sep 2022

Cited by 8 | Viewed by 1717

Abstract

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm [...] Read more.

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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14 pages, 5286 KiB

Open AccessArticle

Optimal Topology Design for Distributed Generation Networks Considering Different Nodal Invulnerability Requirements

by Peng Jiao, Shengjun Huang, Bo Jiang and Tao Zhang

Symmetry 2022, 14(5), 1014; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14051014 - 16 May 2022

Cited by 1 | Viewed by 1465

Abstract

Distributed generators and microgrids are of great importance for the stable operation of power systems when failures occur. The major work of this paper is proposing an optimal topological design model of preset connection lines, aiming at a distributed power generation network based [...] Read more.

Distributed generators and microgrids are of great importance for the stable operation of power systems when failures occur. The major work of this paper is proposing an optimal topological design model of preset connection lines, aiming at a distributed power generation network based on different nodal invulnerability requirements. Moreover, the important innovation of this paper lies in that the perspective is shifted from the system to an individual node of a different type. When a node malfunction occurs, the faulty node can be connected to its neighbor nodes by closing a switch to achieve energy exchange. The distributed generation network consists of a series of nodes that can realize self-sufficiency and can be classified into three types with different levels of importance according to their tasks. The nodes of different types must meet different requirements of destructibility. In this paper, a mixed-integer linear programming model is formulated to solve the optimal topology design problem. In addition, this paper also analyzes the influence of changing nodal power generation capacity and nodal type, and the simulation results show the practicability of the proposal. Full article

(This article belongs to the Special Issue Algorithms for Optimization 2022)

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