•A new method to solve global optimization and engineering problems called OBSCA.•The proposed method improves the SCA by using opposite-based learning.•We apply the OBSCA over mathematical benchmark ...functions.•We test OBSCA in engineering optimization problems.•Comparisons support the improvement on the performance of OBCSA.
Real life optimization problems require techniques that properly explore the search spaces to obtain the best solutions. In this sense, it is common that traditional optimization algorithms fail in local optimal values. The Sine Cosine Algorithms (SCA) has been recently proposed; it is a global optimization approach based on two trigonometric functions. SCA uses the sine and cosine functions to modify a set of candidate solutions; such operators create a balance between exploration and exploitation of the search space. However, like other similar approaches, SCA tends to be stuck into sub-optimal regions that it is reflected in the computational effort required to find the best values. This situation occurs due that the operators used for exploration do not work well to analyze the search space. This paper presents an improved version of SCA that considers the opposition based learning (OBL) as a mechanism for a better exploration of the search space generating more accurate solutions. OBL is a machine learning strategy commonly used to increase the performance of metaheuristic algorithms. OBL considers the opposite position of a solution in the search space. Based on the objective function value, the OBL selects the best element between the original solution and its opposite position; this task increases the accuracy of the optimization process. The hybridization of concepts from different fields is crucial in intelligent and expert systems; it helps to combine the advantages of algorithms to generate more efficient approaches. The proposed method is an example of this combination; it has been tested over several benchmark functions and engineering problems. Such results support the efficacy of the proposed approach to find the optimal solutions in complex search spaces.
•The exploration of whale optimization algorithm is enhanced.•The opposition-based learning is incorporated to the whale optimization algorithm.•The proposed method is tested over benchmark ...optimization functions.•The algorithm is used to estimate parameters of solar cells and photovoltaic panels.•Several comparisons and metrics support the experimental results.
Solar cells are considered as a clean source of energy, and their application includes industrial and domestic users. Most of the algorithms used to design solar cells are tested (and used) only for domestic implementations. However, it is necessary to have accurate mechanisms for solar cell design that can be used in both industrial and domestic energy systems. To achieve this goal, this article introduces an improved version of the whale optimization Algorithm that uses the opposition-based learning to enhance the exploration of the search space. This algorithm is applied to estimate the parameters of solar cells using three different diode models. Such models are the single diode model, the double diode model and the three diode model, each of them has different internal parameters that must be accurately estimated in order to have a good performance of the solar cells. The inclusion of the three diode model is due it represents a more accurate representation of the solar cells behavior in industrial applications. For experiments and comparisons, there are used similar approaches and datasets from solar cells and photovoltaic modules. Moreover, the proposed method has also been tested over different benchmark optimization functions to verify its exploration capabilities. The experiments and comparisons support the performance of the proposed approach in complex optimization problems.
This paper proposes a novel nature-inspired meta-heuristic optimizer, called Reptile Search Algorithm (RSA), motivated by the hunting behaviour of Crocodiles. Two main steps of Crocodile behaviour ...are implemented, such as encircling, which is performed by high walking or belly walking, and hunting, which is performed by hunting coordination or hunting cooperation. The mentioned search methods of the proposed RSA are unique compared to other existing algorithms. The performance of the proposed RSA is evaluated using twenty-three classical test functions, thirty CEC2017 test functions, ten CEC2019 test functions, and seven real-world engineering problems. The obtained results of the proposed RSA are compared to various existing optimization algorithms in the literature. The results of the tested three benchmark functions revealed that the proposed RSA achieved better results than the other competitive optimization algorithms. The results of the Friedman ranking test proved that the RSA is a significantly superior method than other comparative methods. Finally, the results of the examined engineering problems showed that the RSA obtained better results compared to other various methods. Source codes of RSA are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/101385-reptile-search-algorithm-rsa-a-nature-inspired-optimizer
•Developed a novel optimization algorithm inspired by hunting behaviour of Reptiles (RSA).•Tested RSA against classical, CEC2017, CEC2019 test functions and engineering problems.•Compared the RSA to other well-known optimization algorithms.•Demonstrated effectiveness and superiority of the proposed RSA.
The Arithmetic Optimization Algorithm Abualigah, Laith; Diabat, Ali; Mirjalili, Seyedali ...
Computer methods in applied mechanics and engineering,
04/2021, Letnik:
376
Journal Article
Recenzirano
Odprti dostop
This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including ...(Multiplication (M), Division (D), Subtraction (S), and Addition (A)). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and .
This paper improves the performance of the recently-proposed Whale Optimization Algorithm (WOA). WOA is a meta-heuristic that simulates the foraging behavior of humpback whales. There are several ...improvements in the literature for this algorithm of which chaotic maps and Opposition-Based Learning (OBL) are proved to be the most effective. In the former method, however, there are many chaotic maps that make it difficult to choose the best one for a given optimization algorithm. In the latter method, OBL should be applied to a portion of solutions in the population, which is normally obtained manually, which is time-consuming. This work proposed a hyper-heuristic to alleviate these drawbacks by automatically choosing a chaotic map and a portion of the population using the Differential Evolution (DE) algorithm. The proposed algorithm, which called DEWCO, has high ability to improve the exploration and local optima avoidance of WOA. In order to investigate the performance of the proposed DEWCO algorithm, several experiments are conducted on 35 standard CEC2005 functions and using seven algorithms. The experimental results show the superior performance of the proposed DEWCO algorithm to determine the optimal solutions of the test function problems.
•Apply the enhanced crow search algorithm used as Feature selection approach.•Dynamic local neighborhood and adaptive awareness probability used to improve CSA.•Evaluate the performance of the ...proposed method using a set of UCI machine datasets.•Developed approach outperforms other feature selection approaches.
The crow search algorithm (CSA) is a recent metaheuristic inspired by the intelligent group behavior of crows. It has attracted the attention of many researchers because of its simplicity and easy implementation. However, it suffers from premature convergence because of its ability to balance between exploration and exploitation is weak. Therefore, we investigate in this paper, an enhanced version of CSA called by us ECSA as a wrapper feature selection method to extract the best feature subsets. This enhancement achieved by introducing three modifications to the original CSA to improve its performance. Firstly, we propose an adaptive awareness probability to enhance the balance between exploration and exploitation. Secondly, we replace the random choice of the crow to follow by the dynamic local neighborhood to guide the local search. Thirdly, we introduce a novel global search strategy to increase the global exploration capability of the crow. The performance of ECSA is measured using three performance metrics and statistical significance over 16 datasets from the UCI repository. The obtained results are compared with those of the original CSA and some state-of-the-art techniques in the literature. Experimental results showed that ECSA presents a better convergence speed and a better-quality solution.
Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for ...the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.
•Propose a novel FS method, called ISSAFD, which improves Salp Swarm Algorithm (SSA).•ISSAFS enhanced the followers in SSA using SCA and Disrupt operator (DO).•Evaluating the influence of the ...operators of SCA on the behavior of leaders in SSA.•Comparing the performance of ISSAFD with swarm intelligence (SI).•The proposed ISSAFD provided better results in terms of performance measures.
Features Selection (FS) plays an important role in enhancing the performance of machine learning techniques in terms of accuracy and response time. As FS is known to be an NP-hard problem, the aim of this paper is to introduce basically a new variant of Salp Swarm Optimizer (SSA) for FS (called ISSAFD (Improved Followers of Salp swarm Algorithm using Sine Cosine algorithm and Disrupt Operator), that updates the position of followers (F) in SSA using sinusoidal mathematical functions that were inspired from the Sine Cosine Algorithm (SCA). This enhancement helps to improve the exploration phase and to avoid stagnation in a local area. Moreover, the Disruption Operator (Dop) is applied for all solutions, in order to enhance the population diversity and to maintain the balance between exploration and exploitation processes. Two other variants of SSA are developed based on SCA called ISSALD (Improved Leaders of Salp swarm Algorithm using Sine Cosine algorithm and Disrupt Operator) and ISSAF (Improved Followers of Salp swarm Algorithm using Sine Cosine algorithm). The updating process in consists to update the leaders (L) position by SCA and applying (Dop), whereas in ISSAF, the Dop is omitted and the position of followers is updated by SCA. Experimental results are evaluated on twenty datasets where four of them represent high dimensionality with a small number of instances. The obtained results show a good performance of ISSAFD in terms of accuracy, sensitivity, specificity, and the number of selected features in comparison with other metaheuristics (MH).
In recent years, several optimization algorithms are proposed, one of them is Multi-Verse Optimizer (MVO). In this paper, a modified version of MVO is proposed, called CMVHHO, which uses the chaos ...theory and the Harris Hawks Optimization (HHO). The main aim of using the chaotic maps in the proposed method is to determine the optimal value for the parameters of the basic MVO. Besides, the HHO is used as a local search to improve the ability of the MVO to exploit the search space. The performance of the CMVHHO is conducted using a set of chaotic maps to determine the most suitable map, as well as, the different experiments are performed to determine which parameter has the largest effect on the effectiveness of the MVO. Moreover, the performance of the CMVHHO is compared with a set of state-of-the-art algorithms to find the best solution for global optimization problems. Furthermore, the proposed CMVHHO with the best map is applied to solve four well-known engineering problems. The experimental results illustrate that the chaotic Circle map is the best map among all maps because it improved the performance of the CMVHHO, as well as the HHO, affected positively in the behavior of the proposed algorithm. The CMVHHO showed the best results than other algorithms in terms of the performance measures as well as in engineering problems and it outperformed the state-of-the-art algorithms in all problems.
Display omitted
•Performance of passive still, active still, and condenser is studied.•Distilling systems are modeled by different artificial intelligence-based models.•Accumulated productivity of ...active still is improved by 53.21%.•Artificial neural network unified with Harris Hawks optimizer is the best model.•In the best model, R2 is 0.97 and 0.98 for active and passive stills, respectively.
In this paper, a new productivity prediction model of active solar still was developed depending on improving the performance of the traditional artificial neural networks using Harris Hawks Optimizer. This optimizer simulates the behavior of Harris Hawks to catch their prey, and this method is used to determine the optimal parameters of artificial neural networks. The proposed model, called Harris Hawks Optimizer – artificial neural network, is compared with two other models named support vector machine and traditional artificial neural network, in addition to the experimental-based behavior of the solar still. The models were applied to predict the yield of three different distillation systems, namely, passive solar still, active solar still, and active solar still integrated with a condenser. Experimentally, the productivity of the active distiller integrated with the condenser was increased by 53.21% at a fan speed of 1350 rpm. The performance of the models was assessed using different statistical criteria such as root mean square error, coefficient of determination, and others. Among the three models, Harris Hawks Optimizer – artificial neural network had the best accuracy in predicting the solar still yield compared with the real experimental results.
PDF
