Moth Search Algorithm Research Articles

A new approach to predict the missing values of algae during water quality monitoring programs based on a hybrid moth search algorithm and the random vector functional link network

Here, we propose a new alternative machine learning method that combines the advantage of the Random Vector Functional Link Network (RVFL) with Moth Search Algorithm (MSA) to predict the missing values of total algal count during water quality monitoring of surface waters that providing drinking water treatment plants in Fayoum, Egypt. Total of 34 water quality parameters was measured in 270 water samples during the period 2015–2017. The MSA algorithm was used for optimal selection of input features to improve the performance of the RVFL. The predicted missing values of the total algal count, by the proposed MSA-RVFL method, were strongly correlated with the real observed ones. The results of the MSA-RVFL were better than the Support Vector Machine (SVM) and the Adaptive Neural Fuzzy Inference System (ANFIS) models at different sizes of training tests. Compared with GA-RVFL and PSO-RVFL methods, using the MSA-RVFL was relevant to minimize the input variables and to reduce the processing time. The MSA-RVFL model could reduce the number of input variables from thirty-four to eighteen and eventually to four variables. The most significant variables selected by the MSA-RVFL to predict the total algal count were pH, NO3, P and Ca. Based on these four variables, the predicted values of algae were significantly matching with the real observations (R2 = 0.9594). Accordingly, this makes the MSA-RVFL model to be a useful cost-efficient tool during water quality monitoring programs. Finally, the MSA-RVFL showed higher performance whenever the number of inputs is large or small that gives our suggested method more advantages than the traditional ANN models.

Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution

This paper presents an alternative method for cloud task scheduling problem which aims to minimize makespan that required to schedule a number of tasks on different Virtual Machines (VMs). The proposed method is based on the improvement of the Moth Search Algorithm (MSA) using the Differential Evolution (DE). The MSA simulates the behavior of moths to fly towards the source of light in nature through using two concepts, the phototaxis and Levy flights that represent the exploration and exploitation ability respectively. However, the exploitation ability is still needed to be improved, therefore, the DE can be used as local search method. In order to evaluate the performance of the proposed MSDE algorithm, a set of three experimental series are performed. The first experiment aims to compare the traditional MSA and the proposed algorithm to solve a set of twenty global optimization problems. Meanwhile, in second and third experimental series the performance of the proposed algorithm to solve the cloud task scheduling problem is compared against other heuristic and meta-heuristic algorithms for synthetical and real trace data, respectively. The results of the two experimental series show that the proposed algorithm outperformed other algorithms according to the performance measures.

Enhanced Moth Search Algorithm for the Set-Union Knapsack Problems

As an important and novel model with multitudinous practical applications, the set-union knapsack problem (SUKP) is a challenging issue in combinatorial optimization. In this paper, we present an enhanced moth search algorithm (EMS) for solving SUKP, which introduces an enhanced interaction operator (EIO) by integrating differential mutation into the global harmony search and then Levy flight is replaced by EIO. Comparative experimental results, which were conducted on three types of 30 popular SUKP benchmark instances, demonstrate that EMS algorithm is superior to or competitive with the other state-of-the-art metaheuristic algorithm. In particular, EMS reaches the best-known solutions for the great majority of test instances and improves the best-known solutions for six instances. Two critical ingredients of EIO is investigated to confirm their impact on the performance of EMS. The results show that both components have an important role in improving the performance of EMS.

The Importance of Transfer Function in Solving Set-Union Knapsack Problem Based on Discrete Moth Search Algorithm

Moth search (MS) algorithm, originally proposed to solve continuous optimization problems, is a novel bio-inspired metaheuristic algorithm. At present, there seems to be little concern about using MS to solve discrete optimization problems. One of the most common and efficient ways to discretize MS is to use a transfer function, which is in charge of mapping a continuous search space to a discrete search space. In this paper, twelve transfer functions divided into three families, S-shaped (named S1, S2, S3, and S4), V-shaped (named V1, V2, V3, and V4), and other shapes (named O1, O2, O3, and O4), are combined with MS, and then twelve discrete versions MS algorithms are proposed for solving set-union knapsack problem (SUKP). Three groups of fifteen SUKP instances are employed to evaluate the importance of these transfer functions. The results show that O4 is the best transfer function when combined with MS to solve SUKP. Meanwhile, the importance of the transfer function in terms of improving the quality of solutions and convergence rate is demonstrated as well.

Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond

Polycrystalline diamond (PCD) is increasingly becomes an important material used in the industry for cutting tools of difficult-to-machine materials due to its excellent characteristics such as hardness, toughness and wear resistance. However, its applications are restricted because of the PCD material is difficult to machine. Therefore, electrical discharge machining (EDM) is an ideal method suitable for PCD materials due to its non-contact process nature. The performance of EDM, however, is significantly influenced by its process parameters and type of electrode. In this study, soft computing technique was utilized to optimize the performance of the EDM in roughing condition for eroding PCD with copper tungsten or copper nickel electrode. Central composite design with five levels of three machining parameters viz. peak current, pulse interval and pulse duration has been used to design the experimental matrix. The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). It was found that copper tungsten electrode gave lower EWR, in comparison with the copper nickel electrode. The predictive model of radial basis function neural network (RBFNN) was developed to predict the MRR and EWR of the EDM process. The prominent predictive ability of RBFNN was confirmed as the prediction errors in terms of mean-squared error were found within the range of 6.47E−05 to 7.29E−06. Response surface plot was drawn to study the influences of machining parameters of EDM for shaping PCD with copper tungsten and copper nickel. Subsequently, moth search algorithm (MSA) was used to determine the optimal machining parameters, such that the MRR was maximized and EWR was minimized. Based on the obtained optimal parameters, confirmation test with the absolute error within the range of 1.41E−06 to 5.10E−05 validated the optimization capability of MSA.

Binary Moth Search Algorithm for Discounted {0-1} Knapsack Problem

The discounted {0-1} knapsack problem (DKP) extends the classical 0-1 knapsack problem (0-1 KP) in which a set of item groups is included and each group consists of three items, whereas at most one of the three items can be packed into the knapsack. Therefore, the DKP is more complicated and computationally difficult than 0-1 KP. The DKP has been found many applications in real economic problems and other areas. In this paper, the influence of Levy flights operator and fly straightly operator in the moth search (MS) algorithm is verified. Nine types of new mutation operator based on the global harmony search are specially devised to replace Levy flights operator. Then, nine novel MS-based algorithms for DKP are proposed (denoted by MS1–MS9). Extensive experiments on three sets of 30 DKP instances demonstrate the remarkable performance of the proposed nine new MS-based approaches. In particular, it discovers that MS1–MS3 show better comprehensive performance among 10 algorithms. A variety of analyses indicate the important contribution of the individual of memory consideration in MS1–MS9.

Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

Phototaxis, signifying movement of an organism towards or away from a source of light, is one of the most representative features for moths. It has recently been shown that one of the characteristics of moths has been the propensity to follow Levy flights. Inspired by the phototaxis and Levy flights of the moths, a new kind of metaheuristic algorithm, called moth search (MS) algorithm, is developed in the present work. In nature, moths are a family insects associated with butterflies belonging to the order Lepidoptera. In MS method, the best moth individual is viewed as the light source. Some moths that are close to the fittest one always display an inclination to fly around their own positions in the form of Levy flights. On the contrary, due to phototaxis, the moths that are comparatively far from the fittest one will tend to fly towards the best one directly in a big step. These two features correspond to the processes of exploitation and exploration of any metaheuristic optimization method. The phototaxis and Levy flights of the moths can be used to build up a general-purpose optimization method. In order to demonstrate the superiority of its performance, the MS method is further compared with five other state-of-the-art metaheuristic optimization algorithms through an array of experiments on fourteen basic benchmarks, eleven IEEE CEC 2005 complicated benchmarks and seven IEEE CEC 2011 real world problems. The results clearly demonstrate that MS significantly outperforms five other methods on most test functions and engineering cases.