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This paper presents a new agent-based epidemiological model, which is solved using the proposed Hybrid Multi-Swarm Particle Swarm Optimization Algorithm (HMSPSO Algorithm). The HMSPSO is based on a combination of a parallel multi-swarm particle swarm optimization algorithm and real-coded genetic operators, including crossover and mutation. Unlike other well-known particle swarm optimization algorithms, this method uses alternating real-coded heuristic operators applied to parent solutions selected from sub-swarms obtained through agglomerative clustering. The performance of the HMSPSO Algorithm was compared to that of other established single-objective evolutionary algorithms, and the results show that the HMSPSO achieves the best performance in terms of both time efficiency and accuracy. HMSPSO was combined with the developed agent-based epidemiological model. As a result, optimal strategies for anti-epidemic measures such as vaccination intensity, self-quarantine intensity, and other parameters were calculated to maximize the share of surviving individuals.

Association Rule Mining (ARM) can be considered as a combinatorial problem with the purpose of extracting the correlations between items in sizeable datasets. The numerous polynomial exact algorithms already proposed for ARM are unadapted for large databases and especially for those existing on the web. Assuming that datasets are a large space search, intelligent algorithms was used to found high quality rules and solve ARM issue. This paper deals with a cooperative multi-swarm bat algorithm for association rule mining. It is based on the bat-inspired algorithm adapted to rule discovering problem (BAT-ARM). This latter suffers from absence of communication between bats in the population which lessen the exploration of search space. However, it has a powerful rule generation process which leads to perfect local search. Therefore, to maintain a good trade-off between diversification and intensification, in our proposed approach, we introduce cooperative strategies between the swarms that already proved their efficiency in multi-swarm optimization algorithm(Ring, Master-slave). Furthermore, we innovate a new topology called Hybrid that merges Ring strategy with Master-slave plan previously developed in our earlier work [ 23 ]. A series of experiments are carried out on nine well known datasets in ARM field and the performance of proposed approach are evaluated and compared with those of other recently published methods. The results show a clear superiority of our proposal against its similar approaches in terms of time and rule quality. The analysis also shows a competitive outcomes in terms of quality in-face-of multi-objective optimization methods.

Support vector machine (SVM) is considered as one of the most powerful classifiers. They are parameterized models build upon the support vectors extracted during the training phase. One of the crucial tasks in the modeling of SVM is to select optimal values for its hyper-parameters, because the effectiveness and efficiency of SVM depend upon these parameters. This task of selecting optimal values for the SVM hyper-parameters is often called as the SVM model selection problem. Till now a lot of methods have been proposed to deal with this SVM model selection problem, but most of these methods consider the model selection problem in static environment only, where the knowledge about a problem does not change over time. In this paper we have proposed a framework to deal with SVM model selection problem in dynamic environment. In dynamic environment, knowledge about a problem changes over time due to which static optimum values for yper-parameters may degrade the performance of the classifier. For this there should be one efficient mechanism which can re-evaluate the optimal values of hyper-parameters when the knowledge about a problem changes. Our proposed framework uses multi-swarm-based optimization with exclusion and anti-convergence theory to select the optimal values for the SVM hyper-parameters in dynamic environment. The experiments performed using the proposed framework have shown better results in comparison with other techniques like traditional gird search, first grid search, PSO, chained PSO and dynamic model selection in terms of effectiveness and efficiency.

Cloud services gain more attention due to its accessibility, performance, and cost factors. Cloud offers a wide range of services and completes the task without any delay due to its scheduling policies. Task scheduling is an important factor in cloud computing applications. The performance of applications increases due to an effective scheduling strategy. The cloud resources are allocated to the tasks through task scheduling. Factors like customer satisfaction, resource utilization, better performance make task scheduling crucial for service providers. Depending on the scheduling schemes support in clouds, scheduling is categorized into single cloud or multi-cloud scheduling. Multi-cloud environment provides diverse resources and significantly reduces the cost and commercial limitations. However, reducing the cost functions and makespan are the major factors considered to avoid customer dissatisfaction. But it is essential to concentrate on other factors, such as throughput, delay, Makespan, waiting time, response time, utilization, and efficiency to improve the quality of services. This research work presents a Multi-Swarm Optimization model for Multi-Cloud Scheduling for Enhanced Quality of Services for a multi-cloud environment. Experimental results demonstrate that the proposed approach performs better in all aspects compared to existing techniques, such as Adaptive energy-efficient scheduling, single objective particle swarm optimization scheduling, and improves the quality of services.

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm’s efficiency and robustness. Ultimately, an extensive validation of MISWOA through “simulation + experimentation” approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.

Over the last years, recommendation techniques have emerged to cope with the challenging task of optimal service selection, and to help consumers satisfy their needs and preferences. However, most existing models on service recommendation only consider the traditional user-service relation, while in the real world, the perception and popularity of Web services may depend on several conditions including temporal, spatial and social constraints. Such additional factors in recommender systems influence users’ preferences to a large extent. In this paper, we propose a context-aware Web service recommendation approach with a specific focus on time dimension. First, K-means clustering method is hybridized with a multi-population variant of the well-known Particle Swarm Optimization (PSO) in order to exclude the less similar users which share few common Web services with the active user in specific contexts. Slope One method is, then, applied to predict the missing ratings in the current context of user. Finally, a recommendation algorithm is proposed in order to return the top-rated services. Experimental studies confirmed the accuracy of our recommendation approach when compared to three existing solutions.

In this paper, we present a method for electrocardiogram beat based on multi swarm optimization and radial basis function neural network. ECG is a non-surgical method for measuring and recording the electrical activity of the heart and on many occasions, an experienced cardiologist may not be available on the patient’s site. Therefore, a type of automated ECG analysis is required for the patient to take the electrocardiogram by a general practitioner or paramedical team attending the patient’s location. There is a need for automated ECG analysis. Finally, this paper gives the best analysis methodology for the automated analysis of multi-channel ECG signals. Diagnosis may be affected by the presence of artifacts and noise in multi-channel ECG signals. Some researchers calculated dynamic cutoff frequency parameter from noisy ECG signals to remove noise using the neural network method of Radial Basis function with particle swarm improvement method (RBFNN-PSO). But PSO only has Swarm and it takes a lot of time to give a response. To overcome these limitations, an improved version of the RBFNN-PSO algorithm called Radial Basis Function Neural Network with Multi Swarm Optimization (RBFNN-MSO) has been proposed. Finally, the cutoff frequency parameter is determined by the RBFNN-MSO methodology that is applied to digital low-frequency filters for impulse response (FIR). The next step after removing noise from multi-channel ECG signals is the feature extraction and reduction process. 24 features of the patient’s multi-channel ECG signals are extracted. The next part of the research is divided into two steps. The first step is whether or not the patient’s ECG signals are affected. The vector machine is supported with particle swarm improvement (SVM-PSO) and another way is to support the vector machine with multi swarm improvement (SVM-MSO) to detect ECG signals of the affected patient or not. Finally, SVM-MSO offers greater accuracy compared to SVM-PSO. When compared to all other existing architecture results, they used the rating with 86% of all the test accuracy. But in this paper, our proposed work has 90% overall in different situations. In another point of view also,our proposed work has proven that average accuracy is over 85% even then train data set is small.

The complexity of unmanned aerial vehicle (UAV) missions is increasing with the rapid development of UAV technology. Multiple UAVs usually cooperate in the form of teams to improve the efficiency of mission execution. The UAVs are equipped with multiple sensors with complementary functions to adapt to the complex mission constraints. Reasonable task assignment, task scheduling, and UAV trajectory planning are the prerequisites for efficient cooperation of multi-functional heterogeneous UAVs. In this paper, a multi-swarm fruit fly optimization algorithm (MFOA) with dual strategy switching is proposed to solve the multi-functional heterogeneous UAV cooperative mission planning problem with the criterion of simultaneously minimizing the makespan and the total mission time. First, the multi-swarm mechanism is introduced to enhance the global search capability of the fruit fly optimization algorithm. Second, in the smell-based search phase, the local search strategies and large-scale search strategies are designed to drive multiple fruit fly swarms, and the dual strategy switching method is presented. Third, in the vision-based search stage, the greedy selection strategy is adopted. Finally, numerical simulation experiments are designed. The simulation results show that the MFOA algorithm is more effective and stable for solving the multi-functional heterogeneous UAV cooperative mission planning problem compared with other algorithms.

Wireless Sensor Networks (WSN) is composed of a large number of small nodes with limited functionality. The most important issue in this type of networks is energy constraints. In this area several researches have been done from which clustering is one of the most effective solutions. The goal of clustering is to divide network into sections each of which has a Cluster Head (CH). The task of cluster heads collection, data aggregation and transmission to the base station is undertaken. Choosing CHs in WSN in a Non-deterministic Polynomial-hard issue because optimum data collection with effective energy conservation is not capable of being resolved in polynomial time. In the current work, novel variations of Particle Swarm Optimization (PSO) are presented which are particularly formulated for excellent functioning in dynamic settings. The primary notion is the extension of single population PSO as well as charged PSO techniques through the construction of interactive multi-swarms. Updating as well as recalculating algorithms for connected dominating set is also proposed for when topologies of ad hoc wireless networks change. Exhaustive simulations reveal that the suggested method performs excellently in comparison to PSO as well as Hybrid Energy-Effective Distributed clustering protocols.

In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checking, the task of finding specification errors is reduced to deterministic search techniques such as Depth-First Search. Recent research has shown that swarm intelligence offers a powerful search capability compared to traditional techniques. In particular, multi-swarm Particle Swarm Optimization is known to be efficient and can mitigate the state-space explosion problem, i.e., the exponential increase in the search space with a linear increase in the problem size. However, the state-space explosion problem is still significant when verifying very large systems. Further performance improvement is needed. To achieve this, we propose a novel memoization or cache mechanism for storing tentative solutions for reuse in the later stages of the search procedure. For each stage, a candidate solution computed by a swarm is summarized efficiently and heuristically to consolidate similar solutions into a single representative solution. We store the summary and its associated solutions in key-value maps. Instead of computing known solutions repeatedly, we retrieve the solution if the stored key matches the summary. We incorporated the proposed mechanism into a model-checking technique with multi-swarm Particle Swarm Optimization and evaluated the search performance. We show in this paper that the proposed mechanism improved time and space consumption while maintaining solution quality.