Nature-inspired Optimization Method Research Articles

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning.

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.

Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems.

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

Optimization of a three-stage cascade refrigeration system operating with natural refrigerants to produce low temperatures by applying a bio-inspired method

With the growing global energy consumption, on the first hand, and the need to produce low temperatures on the second hand, therefore the technological challenges consist in minimizing greenhouse gas emissions by reducing the energy consumption. In fact, in refrigeration, this consists of the use of environmentally friendly refrigerants (GWP < 150) with the optimization of cold production processes. In this context, several optimization methods have been tested, and the objective of this work to seek the most reliable method to optimize a three-stage cascade vapor compression refrigeration system. Regarding refrigerants, hydrocarbons can be a solution, the R50/R1150/R290 and R50/R1150/R1270 groups are selected and used here to produce temperatures ranging from −160 °C to −120 °C. After comparing seven (7) nature-inspired optimization methods, the JSOA bio-inspired optimization method is applied. Energetic, exergetic and environmental analyzes are also applied to determine the optimal operating point that allows both the minimization of energy consumption and TEWI (Total Equivalent Warming Impact). A pragmatic comparison between the two considered groups provides insights into the choice of high-temperature sub-cycle fluid, and it is found that R290 shows better results compared to R1270.

Deep generative adversarial networks with marine predators algorithm for classification of Alzheimer’s disease using electroencephalogram

Alzheimer's disease (AD) is a neurological disorder characterized by cognitive decline and memory loss. An early and precise diagnosis of Alzheimer's disease is critical for effective therapy and management. The electroencephalogram (EEG) has shown promise as a non-invasive and cost-effective tool for Alzheimer's disease (AD) categorization. The capacity to diagnose Alzheimer's disease at an early stage is one of the benefits of EEG (electroencephalogram) over other methodologies in Alzheimer's disease research. Traditional EEG analysis approaches, such as estimating coherence between various pairs of electrodes, necessitate a significant amount of human labor. We introduce a novel strategy for AD classification based on EEG by combining deep generative adversarial networks (GANs) and the Marine Predators Algorithm (MPA). GANs are powerful deep-learning models that can be trained to generate realistic samples via negative generator and discriminator training. MPA is a nature-inspired optimization method well-known for its ability to solve complicated optimization problems. The proposed system generates synthetic EEG samples using GANs' ability to learn meaningful representations from raw EEG data. The MPA is then employed to enhance classification performance by optimizing the discriminative features extracted by GANs. The MPA simulates the hunting behavior of marine predators, facilitating the exploration of the feature space and identifying significant characteristics for AD categorization. We evaluated the efficacy of the proposed strategy using a publicly accessible EEG database of AD patients and healthy controls. The results of classification precision, sensitivity, and specificity demonstrate that deep GANs with MPA are superior to state-of-the-art techniques.

Intelligent optimization: Literature review and state-of-the-art algorithms (1965–2022)

Today, intelligent optimization has become a science that few researchers have not used in dealing with problems in their field. Diversity and flexibility have made the use, efficiency, and usefulness of various nature-inspired optimization methods, such as evolutionary and meta-heuristic algorithms, more evident in such problems. This work first provides a comprehensive overview of all considerations governing various optimization problems with detailed corresponding categories. Then, the most comprehensive review and recent methods (during 1965–2022) are presented in evolution-based, swarm-based, physics-based, human-based, and hybrid-based categories. More than 320 new algorithms have been reviewed. All specifications including authors, year, abbreviation, inspired source, controls, and their application are considered in this regard. Statistical analyzes of papers and publishers, annually and for 57 years, along with their ranking, are also examined in detail. Among the key achievements of the paper include: the most number of algorithms with 47.71% (156 methods) have been from the swarm category, and most of them were published in the five years of 2021 (72, 22.02%), 2020 (39, 11.93%), 2022 (31, 9.48%), 2019 (26, 7.95%), and 2016 (21, 6.42%) respectively; the top five rankings of publishers of reviewed algorithms/papers were also: “Proceedings of the Congress” (33, 10.09%), “Applied Soft Computing” (19, 5.81%), “Expert Systems with Applications” (18, 5.51%), “Knowledge-Based Systems” (12, 3.67%), “Engineering Applications of Artificial Intelligence” (12, 3.67%), “Advances in Engineering Software” (12, 3.67%), " Neural Computing and Applications " (12, 3.67%), and " Information Sciences " (11, 3.36%). The paper's data is available at: https://github.com/ali-ece.

Optimization of planetary gearbox using nature inspired meta-heuristic optimizers

In this study, a detailed research was carried out to solve the two-stage planetary gearbox design optimization problem by using nature-inspired optimization methods. Seven different nature-inspired meta-heuristic optimizers were applied to minimize the volume of the planetary gear. The recent optimizers, Grey Wolf Optimizer, Artificial Bee Colony, Multi-verse Optimizer, Cuckoo Search, Whale Optimization Algorithm and Salp Swarm Algorithm methods were applied to the problem for the first time. The optimal volume values obtained with the seven different optimizers are much better than the best values known in the literature. The best fitness value obtained is 30% better than the best known in the literature.

Fast random opposition-based learning Golden Jackal Optimization algorithm

Nowadays, optimization techniques are required in various engineering domains in order to find optimal solutions for complex problems. As a result, there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space. The recently designed nature-inspired metaheuristic, named the Golden Jackal Optimization​ (GJO) algorithm, was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems. However, like other approaches, the GJO has the limitations of poor exploitation ability, ease to get stuck in a local optimal region, and an improper balancing of exploration and exploitation. To overcome these limitations, this paper proposes a novel contribution to GJO based on a new technique, namely the fast random opposition-based learning Golden Jackal Optimization algorithm (FROBL-GJO). The FROBL technique is mainly inspired by opposition-based learning (OBL) and random opposition-based learning (ROBL) techniques to enhance the optimization precision and convergence speed of the GJO algorithm. Furthermore, two other models, such as OBL-GJO and ROBL-GJO, are also proposed for comparison purposes. To examine the proficiency of the newly proposed FROBL-GJO algorithm, it has been examined with several well-known existing meta-heuristic algorithms while solving the CEC-2005 and CEC-2019 benchmark test functions and real-life engineering problems. The experimental outcomes and statistical tests reveal the superior performance of the proposed FROBL-GJO in solving both global optimization and engineering design problems. Hence, the findings of benchmark functions and engineering problems endorse that the proposed FROBL-GJO algorithm can be considered a promising method for solving complex optimization problems.

Modified Whale Optimization Algorithm based ANN: a novel predictive model for RO desalination plant

In recent decades, nature-inspired optimization methods have played a critical role in helping industrial plant designers to find superior solutions for process parameters. According to the literature, such methods are simple, quick, and indispensable for saving time, money, and energy. In this regard, the Modified Whale Optimization Algorithm (MWOA) hybridized with Artificial Neural Networks (ANN) has been employed in the Reverse Osmosis (RO) desalination plant performance to estimate the permeate flux (0.118‒2.656 L/h m2). The plant’s datasets have been collected from the literature and include four input parameters: feed flow rate (400‒600 L/h), evaporator inlet temperature (60‒80 °C), feed salt concentration (35‒140 g/L) and condenser inlet temperature (20‒30 °C). For this purpose, ten predictive models (MWOA-ANN Model-1 to Model-10) have been proposed, which are capable of predicting more accurate permeate flux (L/h m2) than the existing models (Response Surface Methodology (RSM), ANN and hybrid WOA-ANN models) with minimum errors. Simulation results suggest that the MWOA algorithm demonstrates a stronger optimization capability of finding the correct weights and biases so as to enable superior ANN based modeling without limitation of overfitting. Ten MWOA-ANN models (Model-1 to Model-10) have been proposed to investigate the plant’s performance. Model-6 with a single hidden layer (H = 1), eleven hidden layer nodes (n = 11) and the thirteen search agents (SA = 13) produced most outstanding regression results (R2 = 99.1%) with minimal errors (MSE = 0.005). The residual errors for Model-6 are also found to be within limits (span of − 0.1 to 0.2). Finally, the findings show that the screened MWOA-ANN models are promising for identifying the best process parameters in order to assist industrial plant designers.

Forecasting Quality of Service (QoS) Parameters of IoT based Web Services with an Artificial Electric Field Algorithm trained Artificial Neural Network

Development of an accurate forecasting model for effective prediction of Quality of Service (QoS) parameters of inter-net of things (IoT) based web services is highly desired, such that it improves service management and user experience. Mostly, QoS parameters are volatile in nature which make the IoT based service and recommendation process chal-lenging. Artificial neural network (ANN) based models are found to be worthy in modeling and forecasting nonlinear QoS parameter sequences. However, improper tuning of ANN parameters with conventional training algorithms may lead to a suboptimal model. Nature-inspired optimization methods are found suitable in fine tuning ANN parameters and have shown proficient results on real-world data mining problems. There is lack of such models for QoS parameters prediction that need to be explored. We develop an Artificial Electric Field Algorithm (AEFA) trained ANN (AEFANN) model for effective and accurate prediction of QoS parameters where AEFA is used to search an optimal ANN structure. The optimal ANN structure is achieved by AEFA through an evolutionary process. Two real-world IoT enabled web service datasets are used for evaluating effectiveness of AEFANN in terms of three performance metrics. Experimental procedures and comparative studies are conducted to establish the superiority of the proposed approach over four other similar forecasts. AEFANN obtained relative worth values of 4.13% ~ 69.12 % (5-min granularity) and 43.32% ~ 80.3 % (1-hr granularity) from SERVICE 1 dataset. Similarly, it obtained relative worth values of 7.25% ~ 65.57 % (5-min granularity) and 43.38% ~ 72.43 % (1-hr granularity) from SERVICE 2 dataset when compared to oth-er models. This is a significant improvement over comparative existing similar model.

A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms.

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model’s parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting.

A Nature-Inspired Metaheuristic Optimization Algorithm Based on Crocodiles Hunting Search (CHS)

The increasing difficulty of actual-world optimization problems has prompted computer researchers to regularly produce additional process improvement techniques. Metaheuristic and evolutionary computing are very popular in nature-inspired optimization methods. This paper introduces the crocodile search algorithm (CHS), which is a revision of a new metaphorical algorithm based on the hunting behavior of crocodile herds. Various adaptive and arbitrary variables are combined within this algorithm to indicate the exploitation and investigation of the exploration area in various discoveries of optimization. The performance of the CHS is measured in different test phases. Initially, a collection of famous experiment events including unimodal, multi-modal, and composite functions are applied to examine exploitation, exploration, local optima avoidance, and convergence of CHS. The CHS algorithm achieves a regular frame for the airfoil with a pretty low drag, which explains that the methods can be efficient while working physical difficulties including restrained plus unknown search.

Implementation of Water Cycle Optimization for Parametric Tuning of PI Controllers in Solar PV and Battery Storage Microgrid System

In this article, a nature-inspired optimization method, based on the water cycle, is implemented for optimal control of a solar photovoltaic microgrid with battery storage. The water cycle optimization is used to tune the parameters of the proportional-integral controllers in the regulation of the battery current and the dc-link voltage. In order to implement the optimization algorithm, the microgrid model is developed and simulated to carry out the cost function to be minimized in the optimization process. The optimization is conducted using different performance indices. The optimized control parameters are validated on a simulated microgrid and an experimental setup. Simulation and experimental results are provided and compared to genetic algorithms and a conventional tuning method to demonstrate the effectiveness of the water cycle technique.

Preface

Due to recent pandemic, 2022 3rd International Conference on Mechanical Instrumentation and Automation (ICMIA 2022) was held virtually online during March 25-27, 2022. The decision to hold the virtual conference was made in compliance with many restrictions and regulations that were imposed by countries around the globe. Such restrictions were made to minimize the risk of people contracting or spreading the COVID-19 through physical contact. There were 80 individuals who attended this on-line conference, represented many countries including Malaysia, Singapore, Indonesia, Brazil and China. ICMIA 2022 is to bring together International Symposium on Mechanical Instrumentation and Automation to a common forum. And another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in International Symposium on Mechanical Instrumentation and Automation and related areas. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&amp;A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the second part, we invited four professors as our keynote speakers. Prof. Hamid Reza Karimi, Politecnico di Milano, Italy. His original research and development achievements span a broad spectrum within the topic of control systems, mechatronics, intelligent systems, and fault diagnosis with applications such as vehicles, robotics and manufacturing systems. And then we had Prof. Na Li, Xidian University, China. Her research focused on the error theory of electronic equipment, multi-scale electromechanical coupling problems, and the design and optimization of new nanoelectronic equipment structures. Assoc. Prof. Mohamed Arezki Mellal, M’Hamed Bougara University, Algeria. His research interests include developing new nature-inspired optimization methods for solving engineering problems of system dependability, design, manufacturing, and energy efficiency. Finally, we had Assoc. Prof. Lisu Yu, Nanchang University, China as our finale keynote speaker. Their insightful speeches had triggered heated discussion in the third session of the conference. Every participant praised this conference for disseminating useful and insightful knowledge. List of Committee member are available in this pdf.

Golden jackal optimization: A novel nature-inspired optimizer for engineering applications

A new nature-inspired optimization method, named the Golden Jackal Optimization (GJO) algorithm is proposed, which aims to provide an alternative optimization method for solving real-world engineering problems. GJO is inspired by the collaborative hunting behaviour of thegolden jackals(Canis aureus). The three elementary steps of algorithm are prey searching, enclosing, and pouncing, which are mathematically modelled and applied. The ability of proposed algorithm is assessed, by comparing with different state of the art metaheuristics, on benchmark functions. The proposed algorithm is further tested for solving seven different engineering design problems and introduces a real implementation of the proposed method in the field of electrical engineering. The results of the classical engineering design problems and real implementation verify that the proposed algorithm is appropriate for tackling challenging problems with unidentified search spaces.

Crow Search Algorithm with Improved Objective Function for Test Case Generation and Optimization

Test case generation and optimization is the foremost requirement of software evolution and test automation. In this paper, a bio-inspired Crow Search Algorithm (CSA) is suggested with an improved objective function to fulfill this requirement. CSA is a nature-inspired optimization method. The improved objective function combines branch distance and predicate distance to cover the critical path on the control flow graph. CSA is a search-based technique that uses heuristic information for automation testing, and CSA optimizers minimize test cases generated by satisfying the objective function. This paper focuses on generating test cases for all paths, including critical paths. The control flow graph covers the information flow among all the classes, functions, and conditional statements and provides test paths. The number of test cases examined through graph path coverage analysis. The minimum number of test paths is counted through complexity metrics using the cyclomatic complexity of the constructed graph. The proposed method is evaluated as mathematical optimization functions to validate their effectiveness in locating optimal solutions. The python codes are considered for evaluation and revealed that our approach is time-efficient and outperforms various optimization algorithms. The proposed approach achieved 100% path coverage, and the algorithm executes and gives optimum results in approximately 0.2745 seconds.

Optimal design of differential mount using nature-inspired optimization methods

Abstract Structural performance and lightweight design are a significant challenge in the automotive industry. Optimization methods are essential tools to overcome this challenge. Recently, nature-inspired optimization methods have been widely used to find optimum design variables for the weight reduction process. The objective of this study is to investigate the best differential mount design using nature-based optimum design techniques for weight reduction. The performances of the nature-based algorithms are tested using convergence speed, solution quality, and robustness to find the best design outlines. In order to examine the structural performance of the differential mount, static analyses are performed using the finite element method. In the first step of the optimization study, a sampling space is generated by the Latin hypercube sampling method. Then the radial basis function metamodeling technique is used to create the surrogate models. Finally, differential mount optimization is performed by using genetic algorithms (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), ant lion optimizer (ALO) and dragonfly algorithm (DA), and the results are compared. All methods except PSO gave good and close results. Considering solution quality, robustness and convergence speed data, the best optimization methods were found to be MFO and ALO. As a result of the optimization, the differential mount weight is reduced by 14.6 wt.-% compared to the initial design.

Three Steps to Improve Jellyfish Search Optimiser

This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.

Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis

One of the most studied application areas of intelligent systems is the classification area, and this is because classification covers a wide range of real-world problems. Some examples are fault-diagnosis, image segmentation, medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on learning how to classify with previous datasets for finding relations between the inputs and outputs. The main focus of the present paper is the supervised generation of general type-2 fuzzy classifiers with a new strategy for modeling data uncertainty. The proposed methodology includes a mix of concepts, such as the use of embedded type-1 membership functions, statistical concepts such as the quartiles, and nature inspired optimization methods. The classifiers generated with the proposed methodology are compared with respect to other general type-2 fuzzy classifiers based on symmetric uncertainty to evaluate their performance, in this way obtaining interesting results for medical diagnosis with benchmark data sets.

Test Case Prioritization Using Bat Algorithm

Background: Regression testing is very important stage of software maintenance and evolution phase. The software keeps on updating and to preserve the software quality, it needs to be retested every time it is updated. Due to limited resources, complete testing of the software becomes tedious task. The probable solution to this problem is to execute those test cases first that are more important prior to less important test cases. Objective: Optimization methods need to be acquired for efficient test case prioritization in minimum time, while maintaining the quality of the software. Various nature-inspired algorithms like genetic algorithm, particle swarm optimization and ant colony optimization, etc. have been applied for prioritizing test cases. In this paper, we have applied a relatively new nature-inspired optimization method, namely, Bat algorithm that utilizes the echolocation, loudness and pulse emission rate of bats in prioritizing the test cases. Methods: The proposed algorithm is experimented on sample case study of timetable management system and evaluated with the help of popular evaluation metric Average Percentage of Fault Detection. Results: Results have been compared with the baseline approaches i.e. untreated, random and reverse prioritization and well-established optimization method i.e. genetic algorithm and we have found a considerable increase in the value of evaluation metric. Conclusion: This preliminary study shows that the bat algorithm have great potential to solve test case prioritization problems.

Selection of optimal hyper-parameter values of support vector machine for sentiment analysis tasks using nature-inspired optimization methods

Sentiment analysis and classification task is used in recommender systems to analyze movie reviews, tweets, Facebook posts, online product reviews, blogs, discussion forums, and online comments in social networks. Usually, the classification is performed using supervised machine learning methods such as support vector machine (SVM) classifier, which have many distinct parameters. The selection of the values for these parameters can greatly influence the classification accuracy and can be addressed as an optimization problem. Here we analyze the use of three heuristics, nature-inspired optimization techniques, cuckoo search optimization (CSO), ant lion optimizer (ALO), and polar bear optimization (PBO), for parameter tuning of SVM models using various kernel functions. We validate our approach for the sentiment classification task of Twitter dataset. The results are compared using classification accuracy metric and the Nemenyi test.