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Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies. This paper focuses on a specific subset: Bio-inspired Population-based Optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper’s focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time. On the other hand, Whale Optimization Algorithm (WOA) has lesser improvement percentage of 6.05% but better optimization time. In conclusion, the standardized approach for benchmarking BPO algorithms provides useful insights into their strengths and challenges in mobile robot path planning.

This study presents a comprehensive analysis of enhancing software effort estimation accuracy using a Self-Organizing Migration Algorithm (SOMA)-optimized Constructive Cost Model (COCOMO). By conducting a comparative study of traditional COCOMO models and SOMA-optimized variants across preprocessed datasets (NASA93, NASA63, NASA18, Kemerer, Miyazaki94, and Turkish), our research focuses on crucial evaluation metrics, including Mean Magnitude of Relative Error (MMRE), Prediction at 0.25 (PRED(0.25)), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The analysis encompasses various configurations of COCOMO models—basic, intermediate, and post-architecture COCOMO II, supplemented with additional statistical testing and residual analysis for in-depth insights. The results demonstrate that the SOMA-optimized COCOMO models generally surpass traditional models in predictive accuracy, especially notable in metrics such as MMRE where an improvement of up to 12%, PRED(0.25) with an enhancement of 15%, MAE reduction by 18%, and a decrease in RMSE by 20% were observed. However, performance variances were identified in specific scenarios, highlighting areas for further refinement, particularly in large-scale estimations where residual plots suggested the potential for underestimation or overestimation. The study concludes that integrating the SOMA optimization algorithm into COCOMO models significantly enhances the accuracy of software effort estimations, providing valuable insights for future research to optimise estimations for larger projects and advance prediction models. This advancement addresses the technical challenge of parameter accuracy and offers a methodological improvement in model selection and application, underscoring the potential of metaheuristic optimization in software effort estimation.

The Industrial Internet of Things (IIoT) is the recent innovation that had revolutionized the industries by enabling interconnected devices and systems to exchange intelligent data. However, implementing and operating such IIoT systems have various challenges. This article addresses those challenges pertained to task offloading in IIoT in which the resource-intensive tasks are transmitted and executed on remote cloud servers. To optimize the task offloading decisions this work propose the integration of Digital Twins, which are the computer-generated replicas of physical objects. By using the functionalities of Digital Twins along with real-time monitoring, and metaheuristic optimization algorithms this work presents a task offloading model for IIoT. Through this combined framework, the proposed model attempts to minimize the task execution time by considering the server capacity, bandwidth constraints, and device power consumption. The proposed Offloading with Digital Twins and Raindrop Algorithm (ODTRA) algorithm that is based on the water cycle metaphor and the Probabilistic Recursive Local (PRL) search algorithm had efficiently optimizes offloading performance which was proven through different experiment simulation and analysis.

To mitigate the rising threat of Android malware, researchers have been actively looking for mechanisms that will enable rapid and accurate malware detection. Recently, attention has been paid to the use of audio-based features derived through the use of music information retrieval techniques. Since the exploration of these features is still in the early stages, there is a need to continue experimentation, especially with features that have yet to be used for this task. In this paper, we present the results of an ongoing investigation into the use of audio-based features for Android malware detection. In addition to extracting new audio-based features, this research aims to find the most discriminative subset of audio-based features through a comparative evaluation of Wrapper-based metaheuristic optimization algorithms on two separate datasets. First, we sonified the Android APK datasets, then extracted 191 static audio-based features from the resultant audio datasets. Fourteen different nature-inspired Wrapper-based metaheuristic optimization algorithms were evaluated for feature selection, and the selected features were then used to train the light gradient-boosting machine (LGBM) classification model. Experimental results demonstrate that the proposed approach exhibits high discriminative capabilities that can outperform other state-of-the-art techniques. The best outcome for Android malware detection was obtained using features selected by the Genetic Algorithm, which achieved 50.26% feature reduction and an improved classification accuracy of 99.72%.

Galactic Swarm Optimization (GSO) is an optimization method inspired by the movements of stars and star clusters in the galaxy. This method aims to find the best solution in two phases using other known optimization methods. The first phase explores the search space, while the second phase tries to refine the best solution. In GSO, the population of the best individuals obtained from the first phase is used as the initial population for the second phase. This process is repeated until the stopping criterion is met. Although the knowledge obtained from the first phase is transferred to the second phase in GSO, there is no knowledge transfer from the second phase to the first phase. In this study, we propose a modification where the knowledge obtained in the second phase is transferred back to the first phase. Additionally, the Particle Swarm Optimization (PSO) method, used as the search method in the original study, has a fast convergence problem, which hinders an effective discovery process in the first phase of GSO. To address this, the proposed diversity-guided modification regulates population diversity and enhances exploration. To demonstrate the performance of the proposed method, twenty-six traditional benchmark functions and three engineering design problems were used. The proposed method was compared with the original GSO and six current optimization methods. The results of the experimental study were analyzed using statistical tests. The experimental results and analyses show that the proposed method performs successfully.

This paper presents an innovative approach to enhance the hybrid Differential Evolution (DE) algorithm by focusing on improved convergence speed, exploration capabilities, and solution precision. This is achieved through the integration of elitism in evolutionary algorithms, gradient methods, and the Metropolis acceptance criterion in Simulated Annealing (SA). We introduce the Gradient Differential Evolution-Simulated Annealing Hybrid (GDESA) framework, refined through parameter sensitivity analysis, which led to the development of two variants: Multi-Phase GDESA (M-GDESA) and Adaptive Elitist GDESA (A-GDESA). M-GDESA improves convergence by adding user-controlled phases but struggles with parameter fine-tuning and semi-static selection. In contrast, A-GDESA allows each individual to dynamically choose between elitist and SA offspring selection schemes at each iteration, enhancing adaptability to diverse problems. Our hybrid frameworks are highly flexible, accommodating any DE variant as the main program. Even with standard DE, our approach is highly competitive on the CEC2017 benchmark for 30-dimensional problems, statistically outperforming existing DE-SA hybrids like iSADE and HDESA, as well as state-of-the-art heuristics such as DE, PSO, ABC, and WOA. Not only does A-GDESA outperform each of the six baselines in at least 24 out of the 29 test functions, but it also achieves the fastest convergence. When using the advanced L-SHADE variant as the main program, our framework outperforms even well-known optimizers and top performers from CEC competitions like L-SHADE and LSHADE-cnEpSin on 100-dimensional problems. The obtained results highlight the effectiveness of the GDESA framework in enhancing DE-SA hybrid optimization and demonstrate the exceptional potential of the self-adaptive scheme with gradient methods.

Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines 1) the characteristics and features of existing routing algorithms, 2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, 3) an analysis of the method’s simulation tools, evaluation metrics and mobility models, 4) the current issues and scope of the study related to the specified method.

Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middle-income countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

Network reconfiguration (NR) is a well-accepted technique to decrease power losses and enhance the voltage profile of the distribution network. The practical challenges in solving the NP-hard NR problem using metaheuristics are to randomly generate and, in each iteration, check and repair non-radial distribution network configurations without compromising on solution space in the least amount of time. Inefficient handling of non-radial configurations results in large computational time and high standard deviation. This paper mitigates the aforementioned challenges by proposing a novel radiality maintenance algorithm (RMA) that involves the novel concept of junction nodes and a selection set to produce only radial configurations. The proposed approach can potentially improve the standard deviation and computational efficiency of metaheuristics in solving the NR problem. The proposed RMA is generic, model-independent, and scalable, as it can be seamlessly integrated into any metaheuristic approach to solve the NR problem involving feeders of different sizes. The proposed RMA, combined with the accelerated particle swarm optimization, is implemented to solve: 1) the standard snapshot NR problem; and 2) the multiperiod co-optimization problem that simultaneously computes optimal network configuration and control setpoints of the photovoltaic system and battery energy storage system. Simulation results suggest a 27.3% standard deviation reduction in achieving the best results reported in the literature on NR-based power loss reduction within a comparable timeframe of 1.6 seconds. The effectiveness and reliability of the proposed algorithm are demonstrated on IEEE 33-bus test system.

Metaheuristics are primarily developed to explore optimization techniques in many practice areas. Metaheuristics refer to computational procedures leading to finding optimal solutions to optimization problems. Due to the increasing number of optimization problems with large-scale data, there is an ongoing demand for metaheuristic algorithms and the development of new algorithms with more efficiencies and improved convergence speed implemented by a mathematical model. One of the most popular optimization problems is job shop scheduling problems. This paper develops a novel metaheuristic hybrid Parthenogenetic Algorithm (NMHPGA) to optimize flexible job shop scheduling problems for single-machine and multi-machine job shops and a furnace model. This method is based on the principles of genetic algorithm (GA), underlying the combinations of different types of selections, proposed ethnic GA, and hybrid parthenogenetic algorithm. In this paper, a parthenogenetic algorithm (PGA) combined with ethnic selection GA is tested; the parthenogenetic algorithm version includes parthenogenetic operators: swap, reverse, and insert. The ethnic selection uses different selection operators such as stochastic, roulette, sexual, and aging; then, top individuals are selected from each procedure and combined to generate an ethnic population. The ethnic selection procedure is tested with the PGA types on a furnace model, single-machine job shops, and multi-machines with tardiness, earliness, and due date penalties. A comparison of obtained results of the established algorithm with other selection procedures indicated that the NMHPGA is achieving better objective functions with faster convergence speed.

The large-scale wind energy conversion systems (WECS) based on a doubly fed induction generator (DFIG) have recently gained attention due to their numerous economic and technological advantages. However, the rapid integration of WECS with standing power networks severely influenced the system’s reliability and stability; also, the DFIG rotor circuit experiences a substantial overcurrent due to grid voltage fluctuations. Indeed, these problems emphasize the significance of a DFIG’s low-voltage ride-through (LVRT) capacity in maintaining the stability of the electrical grid during voltage fluctuations. To solve these challenges simultaneously, this research employs a metaheuristic optimization technique to regulate a doubly fed induction generator’s (DFIG) operation via a wind turbine (WT) system. The article proposes a novel Mountain Gazelle Optimizer (MGO) to optimize the proportional-integral (PI) controller gains for the DFIG system’s active and reactive power control to enhance the LVRT capability of Wind turbines linked to the power grid. In the proposed scheme, LVRT improvement is proportional to undershoot or overshoot, settlement time, and steady-state inaccuracy of voltage responses. The proposed control method is implemented in MATLAB by a detailed model of 9MW wind turbine, and its performance is validated and compared with traditional optimization control approaches. The suggested MGO method’s efficacy is demonstrated by the assessment and comparison to classic optimization-based PI controllers under various fault scenarios. The simulation results show that the optimized control method improved performance in terms of three-phase terminal voltage output responses, active power, reactive power demand to networks, and DC-Link voltage.

Cervical cancer affects nearly 4% of the women across the globe and leads to mortality if not treated in early stage. A few decades before, the mortality rate was too high when compared to the present statistics. This is achieved as nowadays most of women are aware of this disease and undergo health examination mainly for screening cervical cancer on regular basis. But only the accurate diagnosis can be helpful for further treatment. Many works are carried out for accurate diagnosis and always have some limitations in accurate predictions. In this work, an efficient algorithm is proposed for the accurate diagnosis of cervical cancer. A meta-heuristic called artificial Jellyfish search optimizer (JS) algorithm is combined with artificial neural network (ANN) to tackle this problem. The proposed algorithm is called JellyfishSearch_ANN and is employed to classify the cervical cancer dataset with four type of targets based on the examination. The JellyfishSearch_ANN provides outstanding results among other classifiers taken for comparison and mainly its classification accuracy is found to be above 98.87% for all targets.

In the online social networks, blogs, microblogs, social bookmarking services and sharing sites, and various web forum pages; the sharing of knowledge, opinions, ideas, etc. are spreading very quickly. This situation brings very dangerous problems in social networks. One of these problems is hate speech detection (HSD) problem which is covering issues such as insults, swearing, humiliation, discrimination, exclusion, detest, abhor, blast, damn, and intolerance. These can be reactions to a person, a group, an organization, an order, or an event. Although few machine learning methods have been used in the literature to solve this important problem in online social media, the performance of the HSD models in terms of many metrics needs to be increased. In this study, an automatic HSD system based on metaheuristic methodology was proposed for better results in this new and important problem. In the proposed optimization approach, Ant Lion Optimization (ALO) algorithm and Moth Flame Optimization (MFO) algorithm were designed for the HSD problem. This is the first attempt to use optimization algorithms as solution search strategies for automatic HSD. An efficient representation scheme and flexible fitness function were designed for this purpose. Many metrics can easily be embedded into the designed fitness function in order to be simultaneously optimized. Firstly, the basic natural language processing (NLP) steps were carried out. Feature extraction was performed using Bag of Words (BoW), Term Frequency (TF), and document vector (Word2Vec). Then, the performances of the proposed novel approaches were analyzed in detail on the three different real-world data. The obtained results were also checked against eight popular supervised machine learning algorithms, Social Spider Optimization (SSO) algorithm, and state-of-the-art Tunicate Swarm Algorithm (TSA). Considering the evaluation criteria for three sets of experiments, it was observed that the accuracy, sensitivity, precision, and f-score results of the ALO and MFO algorithms were superior to machine learning methods. As a result of the experimental studies, the highest accuracy value was 92.1% for ALO, while this value was 90.7% for MFO. Other numerical values obtained in the study were given in the experiments and results section with tables and graphics in detail. Due to the promising results of the proposed approaches, they are anticipated to be used in the solution of many social media and networking problems.

Planar 3-Degree of Freedom (3-DOF) micropositioning stages are widely adopted in many precision applications for their ability to provide in-plane rotation. However, their motion accuracy is adversely affected by cross-couplings, model uncertainty, and external disturbances. This work proposes an optimized robust control methodology based on disturbance estimation to address these issues. Systematic modeling of a 3-DOF precision micropositioning stage was utilized to develop a nonlinear disturbance observer-based sliding mode control methodology. This methodology can estimate and compensate the unavoidable cross-couplings between the major axes of motion. A stability analysis is conducted to prove the stability of the feedback system when the control approach is combined with the disturbance observer. A maximum bound on the tracking error is derived through finite-time analysis, and the parameters that affect this bound are identified. The antlion optimizer algorithm is used to optimize the control parameter based on the mean square error cost function to eliminate the requirement for manual tuning and to achieve the best attainable performance. The proposed control method is experimentally demonstrated to track complex trajectory with less tracking error than the classical sliding mode approach. The main contribution of this study is the improvement of the trajectory tracking accuracy in multi-DOF micropositioners using an optimized disturbance observer-based robust control technique.

This paper proposes a novel optimization method, namely, hybrid PSOS-CGSA for state estimation in three-phase unbalanced DG-integrated distribution systems. The distribution system state estimation (DSSE) is formulated as a nonlinear optimization problem with constraints where loads and DG outputs are considered as the control variables, while real-time measurements are treated as dependent variables. The proposed DSSE model estimates the loads and DG outputs at each bus by minimizing the difference between the measure and calculated values of the variables monitored in real-time. A novel hybrid algorithm of particle swarm optimization with sigmoid-based acceleration coefficients and chaotic gravitational search algorithm (PSOS-CGSA) is proposed and applied for the DG-integrated DSSE. The feasibility of the proposed approach is verified on the IEEE 13-bus test system, the IEEE 37-bus test system, and the IEEE 123-bus test system. These simulations show that the proposed DSSE model provides reliable and accurate state estimation of DG-integrated distribution systems with a very limited number of real-time measurements at the source substation. The results obtained by proposed hybrid PSOS-CGSA are evaluated by comparing with other methods under the same test conditions, and the obtained results demonstrate the merits of the proposed scheme.

The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.

A feedforward neural network ensemble trained through metaheuristic algorithms has been proposed by researchers to produce a group of optimal neural networks. This method, however, has proven to be very time-consuming during the optimization process. To overcome this limitation, we propose a metaheuristic-based learning algorithm for building an ensemble system, resulting in shorter training time. In our proposed method, a master-slave based metaheuristic algorithm is employed in the optimization process to produce a group of heterogeneous feedforward neural networks, in which the global search operations are executed on the master, and the tasks of objective evaluation are distributed to the slaves (workers). To reduce evaluation costs, the entire training dataset is randomly divided equally into several disjoint subsets. Each subset is randomly paired with another subset of the remainder and distributed to a worker for the objective evaluation. Following the optimization process, representative candidate solutions (individuals) from the entire population are selected to perform as the base components of the ensemble system. The performance of the proposed method has been compared with those of other state-of-the-art techniques in over 31 benchmark regression datasets taken from public repositories. The experimental results show that the proposed method not only reduces the computational time but also achieves significantly better prediction accuracy. Moreover, the proposed method achieved promising results in the application of a subset of the million song dataset, which identifies the release year of a song and predicts the buzz on Twitter.

Feature selection is a challenging step in the field of data mining, because there are many local optimal solutions in a feature space. Feature selection can be considered an optimization problem, which requires as few feature combinations as possible and high accuracy. The binary symbiotic organism search (BSOS) algorithm is proposed in this paper. It maps the symbiotic organism search algorithm from a continuous space to a discrete space using an adaptive S-shaped transfer function and can be used to search for the optimal feature subset in a feature selection space. The proposed BSOS algorithm is evaluated using 19 datasets from the UCI repository. First, the results of four basic S-shaped transfer functions are compared with those of the adaptive S-shaped transfer function. Additionally, the experimental results are compared with the results obtained by the popular binary grasshopper optimization, binary gray wolf optimization, traditional binary particle swarm optimization, and binary differential evolution algorithms, which are also employed for feature selection in the existing literature. The experimental results show that the BSOS algorithm can find the fewest number of features in most datasets and achieve a high classification accuracy. Moreover, the experiments also show that the BSOS algorithm is still at a disadvantage in handling low-dimensional datasets and attains low sensitivity in hyperdimensional datasets.

MapReduce is a popular programming model for big data processing. Although the distributed processing framework Hadoop greatly reduced the development complexity of MapReduce applications, fine tuning of the Hadoop systems for optimal performance remains a major challenge. Configuration tuning is one of the most effective means to improve the performance of MapReduce applications on Hadoop systems, which invariably adopt the default configuration. However, the huge Hadoop configuration parameter space makes it impractical to explore the parameter combinations exhaustively. In this paper, we propose HTune, an effective Hadoop configuration tuning approach for MapReduce applications. We design a nonintrusive performance profiler whose runtime overhead remains less than 2%, to capture the runtime details of the MapReduce applications and generate their performance evaluations. Based on the performance profiles, a two-level fusion model is constructed based on ensemble modeling for each application in the execution predictor, considering both Hadoop configuration, and input data size. Leveraging the execution predictor, a metaheuristic-based configuration optimizer is able to search for the optimal configuration for a given application. Experimental results demonstrate that the optimal Hadoop configuration is often application-specific and data-specific, and it is more suitable to take all relevant configuration parameters into consideration and optimize them together. H-Tune improves the performance of the MapReduce applications by factors of 1.5× and 9.6× on average, respectively, over the state-of-the-art approach and the default configuration.

Nowadays an increasing number of companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a provider to maximize its revenue by intelligently scheduling tasks in its software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, which may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. Its core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. This work compares it with state-of-the-art methods, experimentally. The results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-trip time of tasks for all applications.