In this study, we introduce a new single point metaheuristic optimization approach suitable for both continuous and discrete domains. The proposed algorithm, entitled Blindfolded Spiderman Optimization, follows a piecewise linear search trajectory where each line segment considers a move to an improved solution point. The trajectory resembles spiderman jumping from one building to the highest neighbor building in a blindfolded manner. Blindfolded Spiderman Optimization builds on top of the Buggy Pinball Optimization algorithm. Blindfolded Spiderman Optimization is tested on 16 mathematical optimization functions and one discrete problem of Unbounded Knapsack. We perform a thorough evaluation of Blindfolded Spiderman Optimization against established and state-of-the-art metaheuristic optimization methods, including Whale Optimization, Grey Wolf Optimization, Particle Swarm Optimization, Simulated Annealing, Threshold Accepting, and Buggy Pinball Optimization considering various optimization domains and dimensions. We show that Blindfolded Spiderman Optimization achieves great performance on both continuous and discrete spaces, and superior performance compared to all single-point metaheuristic approaches considered.
This paper proposes an advanced hybrid optimization (GMPA) algorithm to effectively address the inherent limitations of the Grey Wolf Optimizer (GWO) when applied to complex optimization scenarios. Specifically, GMPA integrates essential features from the Marine Predators Algorithm (MPA) into the GWO framework, enabling superior performance through enhanced exploration and exploitation balance. The evaluation utilizes the GTOPX benchmark dataset from the European Space Agency (ESA), encompassing highly complex interplanetary trajectory optimization problems characterized by pronounced nonlinearity and multiple conflicting objectives reflective of real-world aerospace scenarios. Central to GMPA's methodology is an elite matrix, borrowed from MPA, designed to preserve and refine high-quality solutions iteratively, thereby promoting solution diversity and minimizing premature convergence. Furthermore, GMPA incorporates a three-phase position updating mechanism combined with L\'evy flights and Brownian motion to significantly bolster exploration capabilities, effectively mitigating the risk of stagnation in local optima. GMPA dynamically retains historical information on promising search areas, leveraging the memory storage features intrinsic to MPA, facilitating targeted exploitation and refinement. Empirical evaluations demonstrate GMPA's superior effectiveness compared to traditional GWO and other advanced metaheuristic algorithms, achieving markedly improved convergence rates and solution quality across GTOPX benchmarks. Consequently, GMPA emerges as a robust, efficient, and adaptive optimization approach particularly suitable for high-dimensional and complex aerospace trajectory optimization, offering significant insights and practical advancements in hybrid metaheuristic optimization techniques.
Pap smear image segmentation is crucial for cervical cancer diagnosis. However, traditional segmentation models often struggle with complex cellular structures and variations in pap smear images. This study proposes a hybrid Dense-UNet201 optimization approach that integrates a pretrained DenseNet201 as the encoder for the U-Net architecture and optimizes it using the spider monkey optimization (SMO) algorithm. The Dense-UNet201 model excelled at feature extraction. The SMO was modified to handle categorical and discrete parameters. The SIPaKMeD dataset was used in this study and evaluated using key performance metrics, including loss, accuracy, Intersection over Union (IoU), and Dice coefficient. The experimental results showed that Dense-UNet201 outperformed U-Net, Res-UNet50, and Efficient-UNetB0. SMO Dense-UNet201 achieved a segmentation accuracy of 96.16%, an IoU of 91.63%, and a Dice coefficient score of 95.63%. These findings underscore the effectiveness of image preprocessing, pretrained models, and metaheuristic optimization in improving medical image analysis and provide new insights into cervical cell segmentation methods.
Overcrowding in emergency departments (ED) is a persistent problem exacerbated by population growth, emergence of pandemics, and increased morbidity and mortality rates. Thus, automated approaches for ED layout design have recently emerged as promising tools for boosting healthcare service quality. Still, ED design typically involves multiple conflicting objectives, where the interpretability of the associated solutions depends on the availability of intuitive metrics that can capture ED layout complexity. In this paper, we propose graph-theoretic measures to evaluate and rank ED layouts produced by a multi-objective metaheuristic optimization framework with the non-dominated sorting genetic algorithm (NSGA-II) and generalized differential evolution (GDE3). Indeed, Pareto-optimal ED layouts were sought to minimize patient flow cost while maximizing closeness between ED service areas. Then, the layouts were evaluated based on local graph measures (degree centrality, betweenness, clustering coefficient, closeness centrality, nodal strength, and eccentricity) as well as global ones (global efficiency, network characteristic path length and transitivity). Then, a multi-criteria decision-making technique was employed to rank the layouts based on either the objective functions, the graph measures, or combinations of both. The ranking results on a real-world scenario show that the top-ranking layouts are the ones with the best graph-theoretic values. This shows that the graph-theoretic measures can enhance solution interpretability and hence help medical planners in selecting the best layouts. In comparison with the input layout, optimal NSGA-II and GDE3 solutions reduce the patient flow cost by 18.32% and 11.42%, respectively. Also, the two solutions improve the closeness by 14.5% and 18.02%, respectively.
The optimization of nuclear engineering designs, such as nuclear fuel assembly configurations, involves managing competing objectives like reactivity control and power distribution. This study explores the use of Optimization by Prompting, an iterative approach utilizing large language models (LLMs), to address these challenges. The method is straightforward to implement, requiring no hyperparameter tuning or complex mathematical formulations. Optimization problems can be described in plain English, with only an evaluator and a parsing script needed for execution. The in-context learning capabilities of LLMs enable them to understand problem nuances, therefore, they have the potential to surpass traditional metaheuristic optimization methods. This study demonstrates the application of LLMs as optimizers to Boiling Water Reactor (BWR) fuel lattice design, showing the capability of commercial LLMs to achieve superior optimization results compared to traditional methods.
The nature inspired algorithms are becoming popular due to their simplicity and wider applicability. In the recent past several such algorithms have been developed. They are mainly bio-inspired, swarm based, physics based and socio-inspired; however, the domain based on symbiotic relation between creatures is still to be explored. A novel metaheuristic optimization algorithm referred to as Fig Tree-Wasp Symbiotic Coevolutionary (FWSC) algorithm is proposed. It models the symbiotic coevolutionary relationship between fig trees and wasps. More specifically, the mating of wasps, pollinating the figs, searching for new trees for pollination and wind effect drifting of wasps are modeled in the algorithm. These phenomena help in balancing the two important aspects of exploring the search space efficiently as well as exploit the promising regions. The algorithm is successfully tested on a variety of test problems. The results are compared with existing methods and algorithms. The Wilcoxon Signed Rank Test and Friedman Test are applied for the statistical validation of the algorithm performance. The algorithm is also further applied to solve the real-world engineering problems. The performance of the FWSC underscored that the algorithm can be applied to wider variety of real-world problems.
This paper presents the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive foraging, strategic movement, and parasite avoidance behaviors.GOA is designed to balance exploration and exploitation effectively by incorporating three key mechanisms, adaptive foraging for global search, movement toward the best solution for local refinement, and a jump strategy to escape local optima.A solution filtering mechanism is introduced to enhance robustness and maintain population diversity. The algorithm's performance is evaluated on standard unimodal and multimodal benchmark functions, demonstrating significant improvements over existing metaheuristics, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA), and Artificial Bee Colony (ABC). Comparative analysis highlights GOA's superior convergence rate, enhanced global search capability, and higher solution accuracy.A Wilcoxon rank-sum test confirms the statistical significance of GOA's exceptional performance. Despite its efficiency, computational complexity and parameter sensitivity remain areas for further optimization. Future research will focus on adaptive parameter tuning, hybridization with other metaheuristics, and real-world applications in supply chain management, bioinformatics, and energy optimization. The findings suggest that GOA is a promising advancement in bio-inspired optimization techniques.
This thesis concerns the use of reinforcement learning to train neural networks to aid in the design of public transit networks. The Transit Network Design Problem (TNDP) is an optimization problem of considerable practical importance. Given a city with an existing road network and travel demands, the goal is to find a set of transit routes - each of which is a path through the graph - that collectively satisfy all demands, while minimizing a cost function that may depend both on passenger satisfaction and operating costs. The existing literature on this problem mainly considers metaheuristic optimization algorithms, such as genetic algorithms and ant-colony optimization. By contrast, we begin by taking a reinforcement learning approach, formulating the construction of a set of transit routes as a Markov Decision Process (MDP) and training a neural net policy to act as the agent in this MDP. We then show that, beyond using this policy to plan a transit network directly, it can be combined with existing metaheuristic algorithms, both to initialize the solution and to suggest promising moves at each step of a search through solution space. We find that such hybrid algorithms, which use a neural policy trained via reinforcement learning as a core component within a classical metaheuristic framework, can plan transit networks that are superior to those planned by either the neural policy or the metaheuristic algorithm. We demonstrate the utility of our approach by using it to redesign the transit network for the city of Laval, Quebec, and show that in simulation, the resulting transit network provides better service at lower cost than the existing transit network.
The Grid-Forming Inverter (GFMI) is an emerging topic that is attracting significant attention from both academic and industrial communities, particularly in the area of control design. The Decoupled Average Model-based Sliding Mode Current Controller (DAM-SMC) has been used to address the need such as fast response, fixed switching frequency, and no overshoot to avoid exceeding current limits. Typically, the control parameters for DAM-SMC are chosen based on expert knowledge and certain assumptions. However, these parameters may not achieve optimized performance due to system dynamics and uncertainties. To address this, this paper proposes a Particle Swarm Optimization (PSO)-based DAM-SMC controller, which inherits the control laws from DAM-SMC but optimizes the control parameters offline using PSO. The main goal is to reduce chattering and achieve smaller tracking errors. The proposed method is compared with other metaheuristic optimization algorithms, such as Genetic Algorithm (GA) and Simulated Annealing (SA). Simulations are performed in MATLAB/Simulink across various scenarios to evaluate the effectiveness of the proposed controller. The proposed approach achieves a substantial reduction in convergence time, decreasing it by 86.36% compared to the GA and by 88.89% compared to SA. Furthermore, the tracking error is reduced by 11.61% compared to the conventional DAM-SMC algorithm. The robustness of the proposed method is validated under critical conditions, where plant and control model parameters varied by up to 40%.
Evolutionary and bioinspired computation are crucial for efficiently addressing complex optimization problems across diverse application domains. By mimicking processes observed in nature, like evolution itself, these algorithms offer innovative solutions beyond the reach of traditional optimization methods. They excel at finding near-optimal solutions in large, complex search spaces, making them invaluable in numerous fields. However, both areas are plagued by challenges at their core, including inadequate benchmarking, problem-specific overfitting, insufficient theoretical grounding, and superfluous proposals justified only by their biological metaphor. This overview recapitulates and analyzes in depth the criticisms concerning the lack of innovation and rigor in experimental studies within the field. To this end, we examine the judgmental positions of the existing literature in an informed attempt to guide the research community toward directions of solid contribution and advancement in these areas. We summarize guidelines for the design of evolutionary and bioinspired optimizers, the development of experimental comparisons, and the derivation of novel proposals that take a step further in the field. We provide a brief note on automating the process of creating these algorithms, which may help align metaheuristic optimization research with its primary objective (solving real-world problems), provided that our identified pathways are followed. Our conclusions underscore the need for a sustained push towards innovation and the enforcement of methodological rigor in prospective studies to fully realize the potential of these advanced computational techniques.
Path planning is essential for unmanned aerial vehicles (UAVs) as it determines the path that the UAV needs to follow to complete a task. This work addresses this problem by introducing a new algorithm called navigation variable-based multi-objective particle swarm optimization (NMOPSO). It first models path planning as an optimization problem via the definition of a set of objective functions that include optimality and safety requirements for UAV operation. The NMOPSO is then used to minimize those functions through Pareto optimal solutions. The algorithm features a new path representation based on navigation variables to include kinematic constraints and exploit the maneuverable characteristics of the UAV. It also includes an adaptive mutation mechanism to enhance the diversity of the swarm for better solutions. Comparisons with various algorithms have been carried out to benchmark the proposed approach. The results indicate that the NMOPSO performs better than not only other particle swarm optimization variants but also other state-of-the-art multi-objective and metaheuristic optimization algorithms. Experiments have also been conducted with real UAVs to confirm the validity of the approach for practical flights. The source code of the algorithm is available at https://github.com/ngandng/NMOPSO.
Convolutional Neural Networks (CNNs) have been successfully utilized in the medical diagnosis of many illnesses. Nevertheless, identifying the optimal architecture and hyperparameters among the available possibilities might be a substantial challenge. Typically, CNN hyperparameter selection is performed manually. Nonetheless, this is a computationally costly procedure, as numerous rounds of hyperparameter settings must be evaluated to determine which produces the best results. Choosing the proper hyperparameter settings has always been a crucial and challenging task, as it depends on the researcher's knowledge and experience. This study will present work done in recent years on the usage of metaheuristic optimization algorithms in the CNN optimization process. It looks at a number of recent studies that focus on the use of optimization methods to optimize hyperparameters in order to find high-performing CNNs. This helps researchers figure out how to set hyperparameters efficiently.
Adversarial attacks pose significant challenges in many machine learning applications, particularly in the setting of distributed training and federated learning, where malicious agents seek to corrupt the training process with the goal of jeopardizing and compromising the performance and reliability of the final models. In this paper, we address the problem of robust federated learning in the presence of such attacks by formulating the training task as a bi-level optimization problem. We conduct a theoretical analysis of the resilience of consensus-based bi-level optimization (CB$^2$O), an interacting multi-particle metaheuristic optimization method, in adversarial settings. Specifically, we provide a global convergence analysis of CB$^2$O in mean-field law in the presence of malicious agents, demonstrating the robustness of CB$^2$O against a diverse range of attacks. Thereby, we offer insights into how specific hyperparameter choices enable to mitigate adversarial effects. On the practical side, we extend CB$^2$O to the clustered federated learning setting by proposing FedCB$^2$O, a novel interacting multi-particle system, and design a practical algorithm that addresses the demands of real-world applications. Extensive experiments demonstrate the robustness of the FedCB$^2$O algorithm against label-flipping attacks in decentralized clustered federated learning scenarios, showcasing its effectiveness in practical contexts.
The metaheuristic optimization technique attained more awareness for handling complex optimization problems. Over the last few years, numerous optimization techniques have been developed that are inspired by natural phenomena. Recently, the propagation of the new COVID-19 implied a burden on the public health system to suffer several deaths. Vaccination, masks, and social distancing are the major steps taken to minimize the spread of the deadly COVID-19 virus. Considering the social distance to combat the coronavirus epidemic, a novel bio-inspired metaheuristic optimization model is proposed in this work, and it is termed as Social Distancing Induced Coronavirus Optimization Algorithm (COVO). The pace of propagation of the coronavirus can indeed be slowed by maintaining social distance. Thirteen benchmark functions are used to evaluate the COVO performance for discrete, continuous, and complex problems, and the COVO model performance is compared with other well-known optimization algorithms. The main motive of COVO optimization is to obtain a global solution to various applications by solving complex problems with faster convergence. At last, the validated results depict that the proposed COVO optimization has a reasonable and acceptable performance.
Opposition-based learning (OBL) is an effective approach to improve the performance of metaheuristic optimization algorithms, which are commonly used for solving complex engineering problems. This chapter provides a comprehensive review of the literature on the use of opposition strategies in metaheuristic optimization algorithms, discussing the benefits and limitations of this approach. An overview of the opposition strategy concept, its various implementations, and its impact on the performance of metaheuristic algorithms are presented. Furthermore, case studies on the application of opposition strategies in engineering problems are provided, including the optimum locating of control systems in tall building. A shear frame with Magnetorheological (MR) fluid damper is considered as a case study. The results demonstrate that the incorporation of opposition strategies in metaheuristic algorithms significantly enhances the quality and speed of the optimization process. This chapter aims to provide a clear understanding of the opposition strategy in metaheuristic optimization algorithms and its engineering applications, with the ultimate goal of facilitating its adoption in real-world engineering problems.
Optimization plays an important role in tackling public health problems. Animal instincts can be used effectively to solve complex public health management issues by providing optimal or approximately optimal solutions to complicated optimization problems common in public health. BAT algorithm is an exemplary member of a class of nature-inspired metaheuristic optimization algorithms and designed to outperform existing metaheuristic algorithms in terms of efficiency and accuracy. It's inspiration comes from the foraging behavior of group of microbats that use echolocation to find their target in the surrounding environment. In recent years, BAT algorithm has been extensively used by researchers in the area of optimization, and various variants of BAT algorithm have been developed to improve its performance and extend its application to diverse disciplines. This paper first reviews the basic BAT algorithm and its variants, including their applications in various fields. As a specific application, we apply the BAT algorithm to a biostatistical estimation problem and show it has some clear advantages over existing algorithms.
Optimization is critical for optimal performance in deep neural networks (DNNs). Traditional gradient-based methods often face challenges like local minima entrapment. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with population-based optimization algorithms incorporating local search capabilities. Our experiments demonstrate that the proposed method outperforms state-of-the-art gradient-based techniques, such as ADAM, in accuracy and computational efficiency, particularly with high computational complexity and numerous trainable parameters. The results suggest that our approach offers a robust alternative to traditional methods for weight optimization in convolutional neural networks (CNNs). Future work will explore integrating adaptive mechanisms for parameter tuning and applying the proposed method to other types of neural networks and real-time applications.
Educational institutions are essential for economic and social development. Budget cuts in Brazil in recent years have made it difficult to carry out their activities and projects. In the case of expenses with water and electricity, unexpected situations can occur, such as leaks and equipment failures, which make their management challenging. This study proposes a comparison between two machine learning models, Random Forest (RF) and Support Vector Regression (SVR), for water and electricity consumption forecasting at the Federal Institute of Paran\'a-Campus Palmas, with a 12-month forecasting horizon, as well as evaluating the influence of the application of climatic variables as exogenous features. The data were collected over the past five years, combining details pertaining to invoices with exogenous and endogenous variables. The two models had their hyperpa-rameters optimized using the Genetic Algorithm (GA) to select the individuals with the best fitness to perform the forecasting with and without climatic variables. The absolute percentage errors and root mean squared error were used as performance measures to evaluate the forecasting accuracy. The results suggest that in forecasting water and electricity consumption over a 12-step horizon, the Random Forest model exhibited the most superior performance. The integration of climatic variables often led to diminished forecasting accuracy, resulting in higher errors. Both models still had certain difficulties in predicting water consumption, indicating that new studies with different models or variables are welcome.
Metaheuristic algorithms are essential for solving complex optimization problems in different fields. However, the difficulty in comparing and rating these algorithms remains due to the wide range of performance metrics and problem dimensions usually involved. On the other hand, nonparametric statistical methods and post hoc tests are time-consuming, especially when we only need to identify the top performers among many algorithms. The Hierarchical Rank Aggregation (HRA) algorithm aims to efficiently rank metaheuristic algorithms based on their performance across many criteria and dimensions. The HRA employs a hierarchical framework that begins with collecting performance metrics on various benchmark functions and dimensions. Rank-based normalization is employed for each performance measure to ensure comparability and the robust TOPSIS aggregation is applied to combine these rankings at several hierarchical levels, resulting in a comprehensive ranking of the algorithms. Our study uses data from the CEC 2017 competition to demonstrate the robustness and efficacy of the HRA framework. It examines 30 benchmark functions and evaluates the performance of 13 metaheuristic algorithms across five performance indicators in four distinct dimensions. This presentation highlights the potential of the HRA to enhance the interpretation of the comparative advantages and disadvantages of various algorithms by simplifying practitioners' choices of the most appropriate algorithm for certain optimization problems.
This paper discusses research-based findings of applying metaheuristic optimization techniques and nature-inspired algorithms to detect and mitigate phishing attacks. The focus will be on the Grey Wolf nature-inspired metaheuristic algorithm optimized with Random Forest and Support Vector Machine (SVM) classification. Existing research recommends the development and use of nature-inspired detection techniques to solve complex real-world problems. Existing research using nature-inspired heuristics appears to be promising in solving NP-hard problems such as the traveling salesperson problem. In the same classification of NP-hard, is that of cyber security existing research indicates that the security threats are complex and that providing security is an NP-hard problem. This study is expanding the existing research with a hybrid optimization of nature-inspired metaheuristic with existing classifiers (random forest and SVM) for an improvement in results to include increased true positives and decreased false positives. The proposed study will present the importance of nature and natural processes in developing algorithms and systems with high precision and accuracy.