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This work proposes a Reinforced Cuckoo Search Algorithm (RCSA) for multimodal optimization, which comprises three different strategies: modified selection strategy, Patron-Prophet concept, and self-adaptive strategy. The modified selection strategy has been proposed for efficient selection of next generation individuals instead of choosing a random...
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Citations
... including ESOA [20], WSOA [24], RCSA [47], IDARSOA [48], TLMPA [49], EEGWO [50], hHHO-SCA [51], MMPA [38] and ASOINU [52]. According to The ISOA has the best result among all the algorithms, showing its strong competitiveness in searching optimal solution. ...
The seagull optimization algorithm (SOA) is a meta-heuristic algorithm proposed in 2019. It has the advantages of structural simplicity, few parameters and easy implementation. However, it also has some defects including the three main drawbacks of slow convergence speed, simple search method and poor ability of balancing global exploration and local exploitation. Besides, most of the improved SOA algorithms in the literature have not considered the drawbacks of the SOA comprehensively enough. This paper proposes a hybrid strategies based algorithm (ISOA) to overcome the three main drawbacks of the SOA. Firstly, a hyperbolic tangent function is used to adjust the spiral radius. The spiral radius can change dynamically with the iteration of the algorithm, so that the algorithm can converge quickly. Secondly, an adaptive weight factor improves the position updating method by adjusting the proportion of the best individual to balance the global and local search abilities. Finally, to overcome the single search mode, an improved chaotic local search strategy is introduced for secondary search. A comprehensive comparison between the ISOA and other related algorithms is presented, considering twelve test functions and four engineering design problems. The comparison results indicate that the ISOA has an outstanding performance and a significant advantage in solving engineering problems, especially with an average improvement of 14.67% in solving welded beam design problem.
... In this work, we used Cuckoo Search algorithm (CSA) to address the multimodal feature selection issue. The cuckoo search method was chosen because of its independent brooding parasitism behavior, detailed in Thirugnanasambandam et al. (2019). We developed the proposed Binary Reinforced Cuckoo Search Algorithm (BRCSA) to address multimodal optimization in feature selection. ...
Background
Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal.
Methods
In this work, a novel optimization algorithm inspired by cuckoo birds’ behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model’s classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features.
Results
The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.
... In recent years, the cuckoo search (CS) algorithm has been extensively applied in numerical optimization [20,21] and multi-objective optimization [22,23], among other domains. The CS algorithm is widely employed in diverse scenarios to search for robust solutions with fast convergence. ...
To address the drawbacks of the slow convergence speed and lack of individual information exchange in the cuckoo search (CS) algorithm, this study proposes an improved cuckoo search algorithm based on a sharing mechanism (ICSABOSM). The enhanced algorithm reinforces information sharing among individuals through the utilization of a sharing mechanism. Additionally, new search strategies are introduced in both the global and local searches of the CS. The results from numerical experiments on four standard test functions indicate that the improved algorithm outperforms the original CS in terms of search capability and performance. Building upon the improved algorithm, this paper introduces a numerical solution approach for differential equations involving the coupling of function approximation and intelligent algorithms. By constructing an approximate function using Fourier series to satisfy the conditions of the given differential equation and boundary conditions with minimal error, the proposed method minimizes errors while satisfying the differential equation and boundary conditions. The problem of solving the differential equation is then transformed into an optimization problem with the coefficients of the approximate function as variables. Furthermore, the improved cuckoo search algorithm is employed to solve this optimization problem. The specific steps of applying the improved algorithm to solve differential equations are illustrated through examples. The research outcomes broaden the application scope of the cuckoo optimization algorithm and provide a new perspective for solving differential equations.
... On the other hand, it is true to say that metaheuristic optimization algorithms have achieved great success in addressing unimodal optimization problems (Weber et al. 2010;Wang et al. 2015;Sharifi-Noghabi et al. 2017). In recent years, these algorithms have been extended to solve multimodal optimization problems (Li 2007;Orujpour et al. 2019;Wang et al. 2019Wang et al. , 2022Thirugnanasambandam et al. 2019;Farshi and Orujpour 2021;Gong et al. 2022). Niching methods have drawn considerable attention in this regard which includes speciation (Pétrowski 1996;Li et al. 2002;Li andWood 2009), crowding (Mahfoud 1992;Li et al. 2012), restricted tournament selection (Harik 1995), fitness sharing (Goldberg and Richardson 1987;Yin and Germay 1993;Miller and Shaw 1996;Sareni and Krahenbuhl 1998), clearing (Pétrowski 1996;Sacco et al. 2014), clustering (Streichert et al. 2003), derating (Beasley et al. 1993), neighborhood mutation (Epitropakis et al. 2011;Qu et al. 2012a), etc. ...
Due to the multimodal nature of real-world optimization problems, in recent years, there has been a great interest in multi-modal optimization algorithms. Multimodal optimization problems involve identifying multiple local/global optima. Niching techniques have been widely used to tackle multi-modal optimization problems. Most of the existing niching methods either require predefined niching parameters or extra information about the problem space. This paper presents a novel multimodal algorithm based on Butterfly Optimization Algorithm, which is constructed using the Fitness-Distance Balance (FDB) selection method. The purpose of applying the FDB selection method is to discover local/global optima with high potential as a fitness value along with the appropriate distance from solution candidates. Also, a local search scheme is used to enhance the convergence speed of the algorithm. Niching is a technique used in multimodal optimization to maintain diversity among multiple solutions in the population. The minimum distance between the solutions is called a “niche”. The proper niching radius is the main challenge for existing approaches. Knowing the problem space helps determine the niche radius. This paper proposes a new multimodal optimization scheme that does not require prior knowledge of the problem space or the niching parameter. Seven state-of-the-art multi-modal optimization algorithms are compared to the multi-modal butterfly optimization algorithm (MBOA) on 16 benchmarks from the CEC 2013 and CEC 2015 competitions to evaluate its performance. Success rate, Number of function evaluations, Success performance, average number of optima found, Success accuracy, Maximum peak ratio, and Run-time performance criteria were measured over 25 runs to assess the efficiency of the proposed method. The experimental results demonstrate that MBOA outperforms other algorithms according to most of the performance criteria.
... Also, three network planning problems of radio frequency identification (RFID) were used to further evaluate the convergence performance. Thirugnanasambandam et al. [14] presented a reinforced CS algorithm, which included the modified selection method, Patron-Prophet concept and self-adaptive scheme. In the self-adaptive scheme, the step size was regulated dynamically during each iteration. ...
Cuckoo search (CS) algorithm is a simple and effective search technique. However, CS algorithm may suffer from premature convergence as the complexity of the problem increases. To address this challenge, a cuckoo search algorithm with ensemble strategy, called CSES, is presented to strengthen the convergence performance. Specifically, three new search strategies with diverse properties are designed to well balance the trade-off between global exploration and local exploitation. After that, according to the idea of selective ensemble, a priority roulette method is employed to select the appropriate search strategy at different stages of the evolution process, so as to produce more promising results. To investigate the comprehensive performance of CSES algorithm, extensive experiments are carried out on 53 benchmark functions and three chaotic time series prediction problems. Simulation results illustrate that the proposed CSES is superior to six recently developed CS variants and several other advanced evolutionary algorithms.
... Evolutionary algorithms are not only intended to solve mining problems but are also used in other research domains such as mathematical benchmark functions and many more. Other applications can be found in [31][32][33][34]. The intention of this research is to propose an effective multi-objective model for performing HFUI. ...
In itemset mining, the two vital goals that must be resolved from a multi-objective perspective are frequency and utility. To effectively address the issue, researchers have placed a great deal of emphasis on achieving both objectives without sacrificing the quality of the solution. In this work, an effective itemset mining method was formulated for high-frequency and high-utility itemset mining (HFUI) in a transaction database. The problem of HFUI is modeled mathematically as a multi-objective issue to handle it with the aid of a modified bio-inspired multi-objective algorithm, namely, the multi-objective Boolean grey wolf optimization based decomposition algorithm. This algorithm is an enhanced version of the Boolean grey wolf optimization algorithm (BGWO) for handling multi-objective itemset mining problem using decomposition factor. In the further part of this paper decomposition factor will be mentioned as decomposition. Different population initialization strategies were used to test the impact of the proposed algorithm. The system was evaluated with 12 different real-time datasets, and the results were compared with seven different recent existing multi-objective 18112 AIMS Mathematics Volume 8, Issue 8, 18111-18140. models. Statistical analysis, namely, the Wilcoxon signed rank test, was also utilized to prove the impact of the proposed algorithm. The outcome shows the impact of the formulated technique model over other standard techniques.
... In this paper, the Cuckoo Search Algorithm proposed by Author Kalaipriyan T et al. [4] is used to solve the classification problems using FNN. The algorithm is utilized to optimize the weights in FNN. ...
Multi-layer perceptron (MLP) in artificial neural networks (ANN) is one among the trained neural models which can hold several layers as a hidden layer for intensive training to obtain optimal results. On the other hand, the classification problem has a high level of attraction towards researchers to increase the accuracy in classification. In ANN, feedforward neural network (FNN) is one model that possesses the art of solving classification and regression problems. When input data is given to FNN, it will apply the sum of product rule and the activation function to map the input with its appropriate output. In the sum of product rule, a term called weights is to be chosen appropriately to map between the input and output. In standard FNN, the weights are chosen in a random way which may lead to slower convergence towards the optimal choice of weight values. In this paper, an effective optimization model is proposed to optimize the weights of MLP of FNN for effective classification problems. Four different datasets were chosen, and the results are interpreted with statistical performance measures.
... The main aim of the optimization algorithm is to minimize the values of design parameters and the overall cost of the engineering design problem. CEFO has been applied to three famous mechanical engineering design problems, which include welded beam design (WBD), tension/compression spring design (T/CSD) and pressure vessel design (PVD) (Thirugnanasambandam et al. 2019;Verma and Parouha 2021;Ali and Tawhid 2016). The above problems consist of both equality and inequality constraints. ...
The search process in population-based metaheuristic algorithms (MAs) can be classified into two primary behaviours: diversification and intensification. In diversification behaviour, the search space will be explored considerably based on randomization. Whereas intensification alludes to the search for a promising region locally. The success of MAs relies on the balance between two search behaviours. Nonetheless, it is strenuous to get the right balance between these behaviours due to the scholastic nature of MAs. Chaotic maps are proven an excellent tool to enhance both behaviours. This work incorporates the Logistic chaotic map into the recently proposed population-based MA called Electromagnetic field optimization (EFO). This suggested algorithm is named chaotic EFO (CEFO). An improved diversification step with chaos in EFO is presented to efficiently control the global search and convergence to the global best solution. CEFO is tested on different case studies, 40 unconstrained CEC 2014 and CEC 2019 benchmark functions, seven real-world nonlinear systems and three mechanical engineering design frameworks. All experiments are compared with other recent and improved algorithms in the literature to show the performance and effectiveness of the proposed algorithm. Two nonparametric statistical tests, the Wilcoxon rank-sum and the Friedman test, are performed on CEFO and other compared algorithms to determine the significance of the results and show the efficiency of CEFO over other algorithms.
... Thus, the solution is identified as unimproved throughout iterations and discarded. The information on how much it has deviated from the suitable solution can be derived using the proposed model in [61]. In addition, the systematic process of the Patron-Prophet strategy is presented in Figure 1. ...
... Functions F1-F4 represent the unimodal benchmark functions since only one global optimal solution exists in the search space. Unimodal functions were evaluated in this paper to analyze the proposed algorithm's intensification capability [61]. Tables 4 and 5 show that the PP-ABC strategy performs significantly better in determining the optimal solution and is competitive in relation to other existing algorithms. ...
The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC’s performance in higher-dimension situations towards global optima is not on par with other models due to its deficiency in balancing intensification and diversification. In this research, two different strategies are applied for the improvement of the search capability of the ABC in a multimodal search space. In the ABC, the first strategy, Patron–Prophet, is assessed in the scout bee phase to incorporate a cooperative nature. This strategy works based on the donor–acceptor concept. In addition, a self-adaptability approach is included to balance intensification and diversification. This balancing helps the ABC to search for optimal solutions without premature convergence. The first strategy explores unexplored regions with better insight, and more profound intensification occurs in the discovered areas. The second strategy controls the trap of being in local optima and diversification without the pulse of intensification. The proposed model, named the PP-ABC, was evaluated with mathematical benchmark functions to prove its efficiency in comparison with other existing models. Additionally, the standard and statistical analyses show a better outcome of the proposed algorithm over the compared techniques. The proposed model was applied to a three-bar truss engineering design problem to validate the model’s efficacy, and the results were recorded.
... Finally, the discovery probability was adjusted adaptively according to the dimension of the problem. Thirugnanasambandam et al. [17] proposed a reinforced CS for multimodal optimization, in which three different strategies were considered, including the modified selection scheme, Patron-Prophet concept and self-adaptive method. For this selfadaptive strategy, the step size was adjusted dynamically according to the search ability of the algorithm. ...
Cuckoo search (CS) algorithm is an efficient search technique for addressing numerical optimization problems. However, for the basic CS, the step size and mutation factor are sensitive to the optimization problems being solved. In view of this consideration, a new version namely the parameter control based CS (PCCS) algorithm is presented to strengthen the search accuracy and robustness. In this variant, the step size and mutation factor are dynamically updated according to the elite information stored in the historical archives at each generation, so as to realize the reasonable setting of these control parameters. For performance evaluation, numerical experiments are conducted on 25 benchmark functions from two different test suites. Moreover, the application in neural network optimization is also considered to further investigate the effectiveness. Experimental results indicate that the proposed PCCS algorithm is a promising and competitive method in terms of solution quality and convergence rate.
