Adaptive multi-objective swarm intelligence for containerized microservice deployment

Multi-objective swarm intelligence approach for bias mitigation in decision-making software

Gene expression selection for cancer classification using intelligent collaborative filtering and hamming distance guided multi-objective swarm optimization

The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.

The distributed robust optimal allocation method for multi-microgrid interconnected systems usually involves a large number of variables and constraints, and the computational complexity is high in practical applications, which makes it difficult to solve the problem. Therefore, a distributed robust optimal allocation method for multi-microgrid interconnection systems based on multi-objective swarm algorithm is proposed. A distributed robust optimization configuration constraint index model for multi-microgrid interconnection system is established. Considering the influence of energy storage technology operation characteristics on its service life, a micro-grid hybrid energy storage capacity optimization configuration model with the minimum annual comprehensive energy storage cost as the objective function is established with charge and discharge power and residual power as the constraint conditions. The multi-objective swarm algorithm is used to realize the optimization model of distributed robust configuration microgrid interconnection system. By determining the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points, the power capacity configuration of the optimal energy storage system and the corresponding frequency dividing points are determined. The hybrid energy storage configuration model of multi-microgrid interconnection system is established with the minimum alternative operating cost as the objective function, so as to realize the distributed robust optimal configuration of multi-microgrid interconnection system. The simulation results show that the distributed configuration of multi-microgrid interconnection system with the proposed method has good robustness and strong optimization control ability.

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model’s parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms.

PurposeWith the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.Design/methodology/approachThe proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.FindingsThe empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.Originality/valueThis study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

Developing and optimizing a new cogeneration cycle to produce hydrogen from seawater

Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient.

Routing is a persistent concern in wireless sensor networks (WSNs), as getting data from sources to destinations can be a tricky task. Challenges include safeguarding the data being transferred, ensuring network longevity, and preserving energy in harsh environmental conditions. Consequently, this study delves into the suitability of using multi-objective swarm optimization to route heterogeneous WSNs in the hope of mitigating these issues while boosting the speed and accuracy of data transmission. In order to achieve better performance in terms of load balancing and reducing energy expenditure, the MOSSA-BA algorithm was developed. This algorithm combines the Multi-Objective Salp Swarm Algorithm (MOSSA) with the exploiting strategy of the artificial bee colony (BA) in the neighbourhood of Salps. Inspired by the SEP and EDEEC protocols, the integrated solutions of MOSSA-BA were used to route two and three levels of heterogeneous networks. The embedded solutions provided outstanding performance in regards to FND, HND, LND, percentage of remaining energy, and the number of packages delivered to the base station. Compared to SEP, EDEEC, and other competitors based on MOSSA and a modified multi-objective particle swarm optimization (MOPSO), the MOSSA-BA-based protocols demonstrated energy-saving percentages of more than 34% in medium-sized areas of interest and over 22% in large-sized areas of detection.

A new multi-objective minimal-drift maximum power point tracking (MOMD-MPPT) technique is introduced to harness maximum power from single photovoltaic (PV) array fed multilevel converter (MLC). A drift in voltage arises with conventional MPPT techniques, which is minimized in this work with a new multi-objective solution. A new swarm optimization algorithm (NSOA) is used for MPPT, which combines the concept of swarm intelligence and pressure gradient force. It provides a minimal drift phenomenon for the solar MLC. Acceleration factor of the swarm particles in NSOA relies on pressure difference and distance. It leads to smoother convergence towards the operating point without trapping in the local minima. The presented algorithm is compared with well-known MPPT algorithms. Simulated results analyze the performance of MOMD-MPPT considering upper and lower voltage drift for PV fed MLC. Later, this algorithm is experimentally validated for a single PV array fed grid-tied multilevel converter. Recorded results show that the algorithm has minimal drift and improves the system performance in dynamic solar conditions.

Arsenic is a carcinogen, and long-term exposure to it may result in the development of multi-organ disease. Understanding the underlying intricate molecular network of toxicity and carcinogenicity is crucial for identifying a small set of differentially expressed biomarker genes to predict the risk of the exposed population. In this paper, a multiple kernel learning (MKL) embedded multi-objective swarm intelligence technique has been proposed to identify the candidate biomarker genes from the transcriptomic profile of arsenicosis samples. To achieve the optimal classification accuracy along with the minimum number of genes, a multi-objective random spatial local best particle swarm optimization (MO-RSplbestPSO) has been utilized. The proposed MO-RSplbestPSO also guides the multiple kernel learning mechanism which provides data specific classification. The proposed computational framework has been applied to the developed whole genome DNA microarray prepared using blood samples collected from a specific arsenic exposed area of the Indian state of West Bengal. A set of twelve biomarker genes, with four novel genes, are successfully identified for the classification of exposure to arsenic and its subcategories, which can be used as future prognostic biomarkers for screening of arsenic exposed populations. Also, the biological significance of each gene is detailed to delineate the complex molecular networking and mode of toxicity.

Planning a practical three-dimensional (3-D) flight path for unmanned aerial vehicles (UAVs) is a key challenge for the follow-up management and decision making in disaster emergency response. The ideal flight path is expected to balance the total flight path length and the terrain threat, to shorten the flight time and reduce the possibility of collision. However, in the traditional methods, the tradeoff between these concerns is difficult to achieve, and practical constraints are lacking in the optimized objective functions, which leads to inaccurate modeling. In addition, the traditional methods based on gradient optimization lack an accurate optimization capability in the complex multimodal objective space, resulting in a nonoptimal path. Thus, in this article, an accurate UAV 3-D path planning approach in accordance with an enhanced multiobjective swarm intelligence algorithm is proposed (APPMS). In the APPMS method, the path planning mission is converted into a multiobjective optimization task with multiple constraints, and the objectives based on the total flight path length and degree of terrain threat are simultaneously optimized. In addition, to obtain the optimal UAV 3-D flight path, an accurate swarm intelligence search approach based on improved ant colony optimization is introduced, which can improve the global and local search capabilities by using the preferred search direction and random neighborhood search mechanism. The effectiveness of the proposed APPMS method was demonstrated in three groups of simulated experiments with different degrees of terrain threat, and a real-data experiment with 3-D terrain data from an actual emergency situation.

Neural network (NN) based modelling and Multi-objective Swarm Algorithm (MSA) optimization of CNC milling operation

Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm

The way that people moves is changing. Froma sustainability point of view is necessary to put the focus on pedestrians. To reduce pollution and congestion in urban areas, it is necessary moves people with not necessary moves vehicles. This work introduces a particle swarm multi-objective approach that optimizes vehicles and pedestrians traffic urban flow, considering the traffic lights timing. Traffic lights and their scheduling significantly impact vehicles and pedestrian flow in metropolitan cities. From the point of view of the scenario, a large-scale congested urban area is used to test the proposed methodology. The strategy is compared with five state-of-the-art algorithms with satisfactory results.

Improved combined system and application to precipitation forecasting model

This paper presents a coordinated wide-area damping control of power system stabilizers (PSSs), static synchronous compensator (STATCOM), and static synchronous series compensator (SSSC) for grid-forming based wind and solar PV generation resources. The main objective of this work is to enhance the damping for inter-area oscillations while maintaining the voltage and frequency in the prescribed range, taking into consideration the operational uncertainties. Better voltage and reactive power support are provided by the use of STATCOM and for additional damping, an SSSC is employed with the coordinated wide-area damping controller (CWADC). Variable-speed wind turbine control methodology strategically combined with inertia control, de-loading control, pitch angle control, and electronic rotor speed control is employed to obtain an optimum response for discontinuous generation mixes and variable wind speed. The geometric measures of controllability/observability and residues method have been utilized to choose the appropriate input control signals and optimal location of CWADC. A multi-objective salp swarm algorithm has been used to optimize the parameters of the proposed CWADC. To demonstrate the efficacy of the proposed CWADC, nonlinear time-domain simulations are performed on a modified IEEE 68-bus test system embedded with hybrid wind-solar power plants considered multiple time delays.

Color image segmentation using multi-objective swarm optimizer and multi-level histogram thresholding

The majority of water pipelines are subjected to serious deterioration and degradation challenges. This research examines the application of optimized neural network models for estimating the condition of water pipelines in Shaker Al-Bahery, Egypt. The proposed hybrid models are compared against the classical neural network, adaptive neuro-fuzzy inference system, and group method of data handling using four evaluation metrics. These metrics are; Fraction of Prediction within a Factor of Two (FACT2), Willmott's index of agreement (WI), Root Mean Squared Error (RMSE), and Mean Bias Error (MBE). The results show that the neural network trained using Particle Swarm Optimization (PSO) algorithm (FACT2 = 0.93, WI = 0.96, RMSE = 0.09, and MBE = 0.05) outperforms other machine learning models. Furthermore, three multi-objective swarm intelligence algorithms are applied to determine the near-optimum intervention strategies, namely PSO salp swarm optimization, and grey wolf optimization. The performances of the aforementioned algorithms are evaluated using Generalized Spread (GS), Spread (Δ), and Generational Distance (GD). The results yield that the PSO algorithm (GS = 0.54, Δ = 0.82, and GD = 0.01) exhibits better results when compared to the other algorithms. The obtained near-optimum solutions are ranked using a new additive ratio assessment and grey relational analysis decision-making techniques. Finally, the overall ranking is obtained using a new approach based on the half-quadratic theory. This aggregated ranking obtains a consensus index and a trust level of 0.97.