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This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Due to using nature-inspired metaheuristic algorithms to solve real-world applications in different fields, this area has become very popular among researchers because of the flexibility of using optimization algorithms. Therefore, these algorithms can be measured based on their operator, diversity of populations, and balancing between the exploration and exploitation phases. The Grey Wolf Optimization algorithm is one of the competitive metaheuristic algorithms that was proposed in 2014. It mimics the hunting mechanism of wolves. It uses three types of wolves: alpha, beta, and delta for searching in the search space. Despite having competitive performance, GWO has problems regarding local optima and has low exploration. Therefore, enhancement GWO (EGWO) is proposed in this paper in order to solve these problems. EGWO used different methods to improve the performance of GWO: using gamma, z-position, and golden ratio. MGWO is evaluated using CEC2019 ten benchmark functions. Statistical results proved that EGWO outperforms well in six functions out of ten compared to GWO, fitness-dependent optimizer (FDO), cat swarm optimizer, and FOX optimizer. It is also used to solve two real-world applications such as pressure vessel design and 10-bar truss design problems. Results showed that EGWO is very competitive against GWO, FDO, and FOX. [ABSTRACT FROM AUTHOR]

Cuckoo Search Algorithm (CSA) is a nature-inspired metaheuristic optimization algorithm. The optimization algorithm was inspired by the life story of a family of birds called the Cuckoo. In this paper, an application of the CSA, is implemented to estimate model parameters of deep-seated faults by using gravity anomalies. To increase the efficiency of the algorithm, parameter tuning studies are carried out for the optimum algorithm-based control parameters. In addition, the reliability and possible uncertainties of the obtained solutions were examined using probability density function analysis. The model parameters (z1, z2, α, x0 and ρ) for noisy and noise-free synthetic models were obtained by using CSA. These obtained parameters are very compatible with the true model parameters for noise-free data, similarly well-nigh compatible results for noisy data. As a result we can say that the method works successfully when applied to synthetic data. The technique is tested on three widely studied benchmark field data including gravity anomaly [Garber Fault (USA), Gazal Fault (Egypt), Mersa Matruh Fault (Egypt)]. In previous studies, various solution techniques have been used for the inversion of these field data and the compatibility of the model parameters obtained in this study with the values obtained from previous studies was compared. The results demonstrate that the CSA is a very effective and robust approach for the optimization of gravity anomalies. [ABSTRACT FROM AUTHOR]

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy. [ABSTRACT FROM AUTHOR]

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy. [ABSTRACT FROM AUTHOR]

The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents. [ABSTRACT FROM AUTHOR]

The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphids with wings, including the generation of winged aphids, flight mood, and attack mood. Concurrently, the corresponding optimization models are presented according to the above phases. At the phase of the flight mood, according to the comprehensive influence of energy and the airflow, the individuals adaptively choose the flight mode to migrate; at the phase of attack mood, individuals use their sense of smell and vision to locate food sources for movement. Experiments on benchmark test functions and two classical engineering design problems, indicate that the desired AOA is more efficient than other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

In the present paper, a physics-inspired metaheuristic algorithm is presented to solve multi-objective optimization problems. The algorithm is developed based on the concept of Newtonian cooling law that recently has been employed by the thermal exchange optimization (TEO) algorithm to solve single-objective optimization problems efficiently. The performance of the multi-objective version of TEO (MOTEO) is examined through bi- and tri-objective mathematical and engineering problems as well as bi-objective structural design examples. According to the comparisons between the MOTEO and several well-known algorithms, the proposed algorithm can provide quality Pareto fronts with appropriate accuracy, uniformity, and coverage for multi-objective problems. [ABSTRACT FROM AUTHOR]

Motivated by the hunting strategies of cheetahs, this paper proposes a nature-inspired algorithm called the cheetah optimizer (CO). Cheetahs generally utilize three main strategies for hunting prey, i.e., searching, sitting-and-waiting, and attacking. These strategies are adopted in this work. Additionally, the leave the pray and go back home strategy is also incorporated in the hunting process to improve the proposed framework's population diversification, convergence performance, and robustness. We perform intensive testing over 14 shifted-rotated CEC-2005 benchmark functions to evaluate the performance of the proposed CO in comparison to state-of-the-art algorithms. Moreover, to test the power of the proposed CO algorithm over large-scale optimization problems, the CEC2010 and the CEC2013 benchmarks are considered. The proposed algorithm is also tested in solving one of the well-known and complex engineering problems, i.e., the economic load dispatch problem. For all considered problems, the results are shown to outperform those obtained using other conventional and improved algorithms. The simulation results demonstrate that the CO algorithm can successfully solve large-scale and challenging optimization problems and offers a significant advantage over different standards and improved and hybrid existing algorithms. Note that the source code of the CO algorithm is publicly available at https://www.optim-app.com/projects/co. [ABSTRACT FROM AUTHOR]