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Parameter identification for complex systems of nonlinear nature is challenging due to the complicated process structure and large number of parameters with different identifiability. The scope of this work is to develop a model-based parameter identification method for a nonlinear industrial winding system. The proposed parameter identification method consists of two key steps: First, enhanced whale optimization algorithm (EWOA) was proposed to alleviate the issues of low search performance and premature convergence of WOA, for which the following enhancements were made to WOA: (1) improvements to the bubble-net strategy of its mathematical model, (2) amendments to the humpback whales' movements in the direction of the best whales, and (3) imitation of the schooling behavior of humpback whales when chasing prey. Second, EWOA was acted as a training method for artificial neural networks (ANNs)-type multilayer perceptron (MLP), a method referred to as EWOA-MLP. In this, EWOA was applied to train ANNs in order to mitigate the main difficulties of ANNs due to their nonlinear nature and unknown optimal set of control parameters (i.e., weights and biases). The performance of the proposed EWOA-MLP was assessed in modeling the subsystems of the winding process under study and in solving fifteen classification datasets. The effectiveness of EWOA-MLP in both modeling and classification studies was judged by several pertinent assessment metrics. Results of comparison of the proposed EWOA-MLP with other promising methods firmly confirm the promising performance of EWOA-MLP for both local optima avoidance and convergence rate, proving its value and superiority. Moreover, EWOA-MLP was able to outperform other algorithms in modeling the winding process and solving many classification problems. [ABSTRACT FROM AUTHOR]

Software Fault Prediction (SFP) is an important process to detect the faulty components of the software to detect faulty classes or faulty modules early in the software development life cycle. In this paper, a machine learning framework is proposed for SFP. Initially, pre-processing and re-sampling techniques are applied to make the SFP datasets ready to be used by ML techniques. Thereafter seven classifiers are compared, namely K-Nearest Neighbors (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Linear Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The RF classifier outperforms all other classifiers in terms of eliminating irrelevant/redundant features. The performance of RF is improved further using a dimensionality reduction method called binary whale optimization algorithm (BWOA) to eliminate the irrelevant/redundant features. Finally, the performance of BWOA is enhanced by hybridizing the exploration strategies of the grey wolf optimizer (GWO) and harris hawks optimization (HHO) algorithms. The proposed method is called SBEWOA. The SFP datasets utilized are selected from the PROMISE repository using sixteen datasets for software projects with different sizes and complexity. The comparative evaluation against nine well-established feature selection methods proves that the proposed SBEWOA is able to significantly produce competitively superior results for several instances of the evaluated dataset. The algorithms' performance is compared in terms of accuracy, the number of features, and fitness function. This is also proved by the 2-tailed P-values of the Wilcoxon signed ranks statistical test used. In conclusion, the proposed method is an efficient alternative ML method for SFP that can be used for similar problems in the software engineering domain. [ABSTRACT FROM AUTHOR]

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm's primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO's robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively. [ABSTRACT FROM AUTHOR]

In this paper, the β -hill climbing optimizer is hybridized with the flower pollination algorithm (FPA) as a local refinement operator for global optimization problems. The proposed method is called HyFPA β -hc. Such hybridization aims to enhance the balance between exploration and exploitation processes during the search, thus improving the quality of the outcomes. β -hill climbing optimizer is a recent trajectory-based algorithm with a powerful digging the niche to search and find the local optimum, while FPA is a recent population-based algorithm with robust mining several niches in the search space without proper concentration. The proposed HyFPA β -hc is evaluated using 15 unimodal and multimodal test functions established in IEEE-CEC2015. The results show significant improvement in the convergence behaviour of the proposed HyFPA β -hc over FPA using different dimensions of the test function. The comparative evaluation is also conducted against 26 state-of-the-art methods. The experiments consider three problem sizes (with dimensions 10, 30, and 50) to show the proposed HyFPA β -hc performance against all comparative methods, where the proposed method outperformed all compared methods in optimizing 8, 7, 4 out of 15 test functions for 10, 30, 50 dimensions, respectively. Accordingly, the achieved results prove the efficiency of the proposed HyFPA β -hc in optimizing various problem dimensions. In conclusion, the proposed hybrid metaheuristic method can search powerfully in the niches of optimization problems search space and produces very fruitful outcomes. [ABSTRACT FROM AUTHOR]

Capacitated Vehicle routing problem is NP-hard scheduling problem in which the main concern is to find the best routes with minimum cost for a number of vehicles serving a number of scattered customers under some vehicle capacity constraint. Due to the complex nature of the capacitated vehicle routing problem, metaheuristic optimization algorithms are widely used for tackling this type of challenge. Coronavirus Herd Immunity Optimizer (CHIO) is a recent metaheuristic population-based algorithm that mimics the COVID-19 herd immunity treatment strategy. In this paper, CHIO is modified for capacitated vehicle routing problem. The modifications for CHIO are accomplished by modifying its operators to preserve the solution feasibility for this type of vehicle routing problems. To evaluate the modified CHIO, two sets of data sets are used: the first data set has ten Synthetic CVRP models while the second is an ABEFMP data set which has 27 instances with different models. Moreover, the results achieved by modified CHIO are compared against the results of other 13 well-regarded algorithms. For the first data set, the modified CHIO is able to gain the same results as the other comparative methods in two out of ten instances and acceptable results in the rest. For the second and the more complicated data sets, the modified CHIO is able to achieve very competitive results and ranked the first for 8 instances out of 27. In a nutshell, the modified CHIO is able to efficiently solve the capacitated vehicle routing problem and can be utilized for other routing problems in the future such as multiple travelling salesman problem. [ABSTRACT FROM AUTHOR]

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86 % using only 24 sensors with AR 20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas. [ABSTRACT FROM AUTHOR]

The Coronavirus herd immunity optimizer (CHIO) is a new human-based optimization algorithm that imitates the herd immunity strategy to eliminate of the COVID-19 disease. In this paper, the coronavirus herd immunity optimizer (CHIO) is modified to tackle a discrete power scheduling problem in a smart home (PSPSH). PSPSH is a combinatorial optimization problem with NP-hard features. It is a highly constrained discrete scheduling problem concerned with assigning the operation time for smart home appliances based on a dynamic pricing scheme(s) and several other constraints. The primary objective when solving PSPSH is to maintain the stability of the power system by reducing the ratio between average and highest power demand (peak-to-average ratio (PAR)) and reducing electricity bill (EB) with considering the comfort level of users (UC). This paper modifies and adapts the CHIO algorithm to deal with such discrete optimization problems, particularly PSPSH. The adaptation and modification include embedding PSPSH problem-specific operators to CHIO operations to meet the discrete search space requirements. PSPSH is modeled as a multi-objective problem considering all objectives, including PAR, EB, and UC. The proposed method is examined using a dataset that contains 36 home appliances and seven consumption scenarios. The main CHIO parameters are tuned to find their best values. These best values are used to evaluate the proposed method by comparing its results with comparative five metaheuristic algorithms. The proposed method shows encouraging results and almost obtains the best results in all consumption scenarios. [ABSTRACT FROM AUTHOR]