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Expanding the capacity of optimization algorithms for simultaneous optimization of multiple competing objectives is a crucial aspect of research. This study presents MnMOMFO, a novel non-dominated sorting (NDS) and crowding distance (CD)-based multi-objective variant of the moth-flame optimization (MFO) algorithm for multi-objective optimization problems. The algorithm incorporates arithmetic and geometric mean concepts to address MFO's limitations and to improve its performance. Subsequently, we extend this enhanced MFO into a multi-objective variant, leveraging NDS and CD strategies to achieve a well-distributed Pareto optimal front. The effectiveness of the proposed MnMOMFO algorithm is rigorously evaluated across three distinct phases. In the initial phase, we scrutinize its performance on four ZDT multi-objective optimization problems, employing four performance metrics—general distance, inverted general distance, spacing, and spread metric. Comparative analyses with select competitive multi-objective optimization algorithms comprehensively understand MnMOMFO's efficacy. Secondly, 24 complex multi-objective IEEE CEC 2020 test suits are considered on two performance metrics. Namely, Pareto sets proximity and the inverted generational distance in decision space. In the third phase, five real-world engineering problems are considered to measure the problem-solving ability of the MnMOMFO algorithm. The results from the experiments indicated that the MnMOMFO was the best candidate algorithm, achieving more than 95% superior results for multi-objective ZDT benchmark problems, IEEE CEC 2020 test functions, and real-life issues in contrast to several other algorithms. The experimental outcomes substantiate MnMOMFO's superiority, establishing it as a robust and efficient algorithm for multi-objective optimization challenges with broad applicability to real-world engineering problems. [ABSTRACT FROM AUTHOR]

For the simultaneous optimization of many conflicting objectives, the capability of optimization algorithms must be increased. In this research, a non-dominated sorting (NDS) and crowding distance (CD)-based multi-objective variant of an advanced moth flame optimization (MFO) has been offered. Firstly, a mathematical quasi-reflection-based learning (QRL) strategy and weight strategy (WS) have been added to the classical MFO to alleviate the drawbacks of MFO. Next, this advanced MFO has been extended into multi-objective variant, namely MOQRMFO, with the help of NDS and CD approaches for well-distributed Pareto optimal front. The efficiency of the suggested MOQRMFO algorithm is tested in three phases. In the first phase, five ZDT multi-objective optimization problems (MOOPs) were considered under four performance metrics, namely general distance (GD), inverted general distance (IGD), spacing (S) and spread metric (Δ ) and then compared it with competitive multi-objective optimization algorithms. Secondly, six problems from the DTLZ test suite are considered under the above performance metrics to examine the effectiveness of the suggested MOQRMFO algorithm. The MOQRMFO algorithm's capacity to solve real-world problems has been evaluated by considering four multi-objective real-world engineering design issues in the third phase using several performance metrics. The experimental outcomes show that the MOQRMFO is a better candidate algorithm achieving more than 95% superior results for multi-objective benchmarks and real-life problems in contrast to several other algorithms. [ABSTRACT FROM AUTHOR]

This paper provides a novel, unique, and improved optimization algorithm called the modified Orca Predation Algorithm (mOPA). The mOPA is based on the original Orca Predation Algorithm (OPA), which combines two enhancing strategies: Lévy flight and opposition-based learning. The mOPA method is proposed to enhance search efficiency and avoid the limitations of the original OPA. This mOPA method sets up to solve the global optimization issues. Additionally, its effectiveness is compared with various well-known metaheuristic methods, and the CEC'20 test suite challenges are used to illustrate how well the mOPA performs. Case analysis demonstrates that the proposed mOPA method outperforms the benchmark regarding computational speed and yields substantially higher performance than other methods. The mOPA is applied to ensure that all load demand is met with high reliability and the lowest energy cost of an isolated hybrid system. The optimal size of this hybrid system is determined through simulation and analysis in order to service a tiny distant location in Egypt while reducing costs. Photovoltaic panels, biomass gasifier, and fuel cell units compose the majority of this hybrid system's configuration. To confirm the mOPA technique's superiority, its outcomes have been compared with the original OPA and other well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Breast cancer is a significant health concern due to its aggressive nature and high mortality rates. Early detection is crucial to improving patient outcomes. Thermography, a non-invasive and cost-effective method, utilizes heat from the breast surface to detect abnormalities, with the Database for Mastology Research (DMR) leveraging infrared images for research purposes. Deep learning (DL), particularly convolutional neural networks (CNNs), shows promise in accurately classifying breast cancer images. However, CNNs face challenges with hyperparameters. To address these challenges, this article proposes a new DL model, the optimized DenseNet model (LFR-COA-DenseNet121-BC), incorporating a boosted metaheuristic algorithm called LFR-COA. This algorithm, a developed version of the Coati Optimization Algorithm (COA), integrates Random opposition-based learning (ROB), Brownian motion, and Lévy Flight (LF) schemes. The proposed model achieved impressive results, accurately classifying 99.97% of the test set. Comparison with established models such as VGG16, VGG19, DenseNet201, InceptionV3, Xception, and MobileNet revealed superior performance of the LFR-COA-DenseNet121-BC model, with 99.97% accuracy, 99.96% sensitivity, and 99.9% specificity. Further comparison with COA-DenseNet121 highlighted the superiority of LFR-COA-DenseNet-BC. In addition, LFR-COA was evaluated at the IEEE Congress on Evolutionary Computation held in 2022 (CEC 2022), and real-world medical scenarios showcased the effectiveness of LFR-COA compared to existing optimization algorithms. LFR-COA consistently outperformed the original COA algorithm and other well-known counterparts in various statistical, convergence, and diversity measures, affirming its efficacy in breast cancer classification. • CEC'22 test suite is utilized for verification of LFR-COA performance. • The LFR-COA algorithm optimizes various hyperparameters of the DenseNet121 model. • LFR-COA algorithm is proposed for solving global optimization problems. • LFR-COA-DenseNet121-BC: a new diagnostic model for breast cancer using DMR-IR dataset. • The proposed model surpasses other SOTA methods. [ABSTRACT FROM AUTHOR]

The COVID-19 is one of the most significant obstacles that humanity is now facing. The use of computed tomography (CT) images is one method that can be utilized to recognize COVID-19 in early stage. In this study, an upgraded variant of Moth flame optimization algorithm (Es-MFO) is presented by considering a nonlinear self-adaptive parameter and a mathematical principle based on the Fibonacci approach method to achieve a higher level of accuracy in the classification of COVID-19 CT images. The proposed Es-MFO algorithm is evaluated using nineteen different basic benchmark functions, thirty and fifty dimensional IEEE CEC'2017 test functions, and compared the proficiency with a variety of other fundamental optimization techniques as well as MFO variants. Moreover, the suggested Es-MFO algorithm's robustness and durability has been evaluated with tests including the Friedman rank test and the Wilcoxon rank test, as well as a convergence analysis and a diversity analysis. Furthermore, the proposed Es-MFO algorithm resolves three CEC2020 engineering design problems to examine the problem-solving ability of the proposed method. The proposed Es-MFO algorithm is then used to solve the COVID-19 CT image segmentation problem using multi-level thresholding with the help of Otsu's method. Comparison results of the suggested Es-MFO with basic and MFO variants proved the superiority of the newly developed algorithm. [ABSTRACT FROM AUTHOR]