Enhanced Particle Swarm Optimization Research Articles

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

An expert system for highway construction: Multi-objective optimization using enhanced particle swarm for optimal equipment management

Expert systems play a crucial role in decision-making across various industries. This study introduces a novel expert system employing a tailored multi-objective optimization (MOO) model to address the intricate demands of highway projects. Integrating an advanced Enhanced Particle Swarm Optimization (IPSO) strategy, our model emphasizes key operations like excavation, hauling, grading, and compaction. Considering factors such as equipment count, velocity, and capacity, the system provides a comprehensive set of optimal solutions and reveals the Pareto frontier. In benchmarking our Improved Particle Swarm Optimization (IPSO) model against established methods, such as Genetic Algorithms (GA) by various researchers and the Guided Population Archive Whale Optimization Algorithm (GPAWOA), our approach significantly excelled. Our model demonstrated a 35.4%-time reduction and a 39.1% cost reduction while enhancing operational quality—starkly contrasting the modest improvements seen with other methods. This showcases the IPSO model's robustness in optimizing construction equipment utilization beyond current standards. Functioning as a decision-support tool, the expert system aids stakeholders in selecting optimal equipment setups, considering diverse attributes from equipment specifications to speed and volume. This application is exemplified through a real-world highway construction case study. The Case Study showcases the proposed model's remarkable impact, yielding a 35.4% time saving and a 16.8% cost decrease with Optimal Solution (I). In contrast, Optimal Solution (III) required 144.6% more time yet reduced costs by 39.1%. Furthermore, Optimal Solution (II) achieved a 4.6%-time reduction and a 32.4% cost decrease, demonstrating the system's versatility in managing constraints and optimizing decision-making processes across various project stages.

Detent Force Reduction in Tubular Permanent Magnet Linear Synchronous Machine Using Particle Swarm Optimization

Compared to rotary machines, tubular permanent magnet linear synchronous machine (TPMLSM) has many advantages, such as simple construction and excellent dynamic performance. In the current paper, an enhanced particle swarm optimization (PSO) algorithm is utilized to minimize the detent force of TPMLSM. The proposed optimization method can automatically determine the number of particles to reduce the number of particles and promote computational efficiency. Specifically, maintaining the number of particles in the exploration phase and alleviating the number of particles in the exploitation stage, and the stage in exploitation or exploration, are determined by the difference between the gbest’s present and past cost values. The algorithm’s efficiency is evaluated on the six-hump camel back, Goldstein-Price, and 3-D Hartman functions. Then, the TPMLSM is optimized using the improved PSO algorithm, aiming at minimizing the detent force. The calculation results indicate that the value of detent force is reduced 56.3 and 64.8% for positive maximum and negative maximum value, respectively. Finally, a prototype was manufactured according to the optimized dimensions and structure, and an experimental platform was constructed to verify the TPMLSM’s electromagnetic performance. The experimental test verifies the feasibility and design validity of the optimization algorithm.

An enhanced particle swarm optimization with position update for optimal feature selection

In recent years, feature selection research has quickly advanced to keep up with the age of developing expert systems. This is because the applications of these systems sometimes need massive datasets. Researchers who have an interest in creating novel feature selection methods or enhancing existing technologies have grown their interest in this topic. The current version of binary PSO (BPSO) is not developed as well as continuous PSO and does not follow the main principles of the standard PSO algorithm. Unlike the continuous version of the PSO, in BPSO, particle position is restricted to the Hamming space. Thus, there is no risk of swarm divergence, but the problem of premature convergence of the swarm arises. This paper presents an enhanced particle swarm optimization employed for feature selection to tackle the limitations of the current version of binary BPSO. Its primary quality is the application of a position update mechanism to appropriately select optimal features. The position update mechanism is designed to avoid the premature convergence issue suffered by BPSO, by determining the probability of the particles switching positions using position values rather than velocity. Two variant methods for determining the probability of changing the position of a particle element were introduced. These result in the two variants of enhanced binary particle swarm optimization (EBPSO) for feature selection, called EBPSO1 and EBPSO2. Several experiments were performed to analyze the effectiveness of the proposed method and compare it with state-of-the-art feature selection methods based on eighteen frequently used benchmark datasets. The experimental findings demonstrated that the proposed approach generated the most accurate classification in contrast to the alternative feature selection approach on most of the datasets. As a result, it can be inferred based on the outcomes obtained that this proposed approach performs significantly superior to the other state-of-the-art feature selection approaches.

A Multi-Objective Optimization Method for Maritime Search and Rescue Resource Allocation: An Application to the South China Sea

To effectively address the increase in maritime accidents and the challenges posed by the trend toward larger ships for maritime safety, it is crucial to rationally allocate the limited maritime search and rescue (MSAR) resources and enhance accident response capabilities. We present a comprehensive method for allocating MSAR resources, aiming to improve the overall efficiency of MSAR operations. First, we use long short-term memory to predict the number of future accidents and employ the K-medoids algorithm to identify the accident black spots in the studied area. Next, we analyze the multi-constraint conditions in the MSAR resource allocation process. A multi-objective integer programming model is constructed to minimize the response time and allocation cost. Finally, we use the non-dominated sorting genetic algorithm II (DNSGA-II) with Deb’s rules to solve the model, and we propose a multi-attribute decision optimization-based method for MSAR resource allocation. We found that the DNSGA-II exhibits better convergence and generates higher-quality solutions compared to the NSGA-II, particle swarm optimization (PSO), and enhanced particle swarm optimization (EPSO) algorithms. Compared with the existing MSAR resource emergency response system, the optimized scheme reduces the response time and allocation cost by 11.32% and 6.15%, respectively. The proposed method can offer decision makers new insights when formulating MSAR resource allocation plans.

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

PsoResNet: An improved PSO-based residual network search algorithm

Neural Architecture Search (NAS) methods are widely employed to address the time-consuming and costly challenges associated with manual operation and design of deep convolutional neural networks (DCNNs). Nonetheless, prevailing methods still encounter several pressing obstacles, including limited network architecture design, excessively lengthy search periods, and insufficient utilization of the search space. In light of these concerns, this study proposes an optimization strategy for residual networks that leverages an enhanced Particle swarm optimization algorithm. Primarily, low-complexity residual architecture block is employed as the foundational unit for architecture exploration, facilitating a more diverse investigation into network architectures while minimizing parameters. Additionally, we employ a depth initialization strategy to confine the search space within a reasonable range, thereby mitigating unnecessary particle exploration. Lastly, we present a novel approach for computing particle differences and updating velocity mechanisms to enhance the exploration of updated trajectories. This method significantly contributes to the improved utilization of the search space and the augmentation of particle diversity. Moreover, we constructed a crime-dataset comprising 13 classes to assess the effectiveness of the proposed algorithm. Experimental results demonstrate that our algorithm can design lightweight networks with superior classification performance on both benchmark datasets and the crime-dataset.

Implementation of Network Security for Intrusion Detection & Prevention System in IoT Networks: Challenges & Approach

EPSO-GPSR (Enhanced Particle Swarm Optimization-based Load Balancing with Geographic Routing using Greedy Perimeter Stateless Routing), a unique strategy designed specifically for WSNs, is presented in this work. Using Enhanced Particle Swarm Optimization (EPSO) to provide load balancing across sensor nodes, the proposed EPSO-GPSR technique reduces energy disparities and increases the operational lifetime of the network. Additionally, it interfaces with the geographic routing protocol Greedy Perimeter Stateless Routing (GPSR) to enable effective data forwarding based on geographic locations, minimizing communication overhead and improving scalability. EPSO-GPSR's efficacy is shown against traditional load balancing and routing methods via comprehensive simulations and performance assessments. The network lifetime, energy efficiency, throughput, packet delivery ratio, and delay have all significantly showed better performance, according to the results. Additionally, the EPSO-GPSR algorithm demonstrates robustness against node failures and issues related to scalability, indicating a significant potential for real-world implementation in various WSN scenarios.

Enhanced particle swarm optimization and P&O for MPPT of photovoltaic systems under partial shading conditions

Although Photovoltaic Technologies are largely deployed as a renewable energy source, several factors affect their performance. The major factors that affect PV performance are changes in irradiance and temperature. Maximum PowerPoint Tracking of PV output is essential in giving the maximum photovoltaic outputs at variable levels. Instantaneous variation in irradiance and temperature increases the complexity of tracking maximum power points. Partial shading conditions resulting from shade from trees, tall buildings, and Cloud formation amongst others greatly affect PV systems, especially in large Photovoltaic systems. Under the Partial shading condition, P-V curves become more complex as it is characterized by multiple peaks. The conventional PSO is associated with less accuracy in tracking the Global Maximum Power Point (Global MPP) and slow convergence time in obtaining the Global MPPT and oscillations. In this thesis, a modified Particle Swarm Optimization based Maximum Power Point Tracking technique is designed in MATLAB/Simulink to track the global Maximum Power Point of a Photovoltaic system under partial shading. The proposed modified PSO combines conventional PSO and P&O methods. The particle position in the PSO method is given as the duty cycle value d of the DC-DC converter. Conventional PSO equations are used to update the velocities and duty cycle. Thereafter, the maximum velocity and duty cycle are perturbed to reduce convergence time. The designed PV system was simulated in a MATLAB environment for 10 different irradiation levels and results were compared with results from related works. The average convergence time was 0.99 seconds and efficiency was up to 99.8% with the proposed model which performed better than conventional methods.

Multi-objective Collaborative Optimization Method for Intelligent Distribution Network Operation and Maintenance

Most of the conventional multi-objective coordination methods for active distribution network operation are designed based on the principle of weighted random algorithm, which has limited application scope and cannot simplify the algorithm and reduce the complexity of the solution process, resulting in poor stability of active distribution network lines and potential faults. Based on this, this algorithm is introduced to carry out multi-objective collaborative optimization for intelligent distribution network operation. Firstly, the node types for the intelligent distribution network are defined and the power flow of the distribution network is analyzed. Secondly, a multi-objective configuration model for the lower level of the operation is constructed to maximize the distribution network investment and operation benefit index. A multi-objective collaborative optimization algorithm based on an enhanced particle swarm optimization algorithm is designed to reduce the operation cost of the distribution network and enhance the security and stability of the distribution network system during operation. The simulation test results show that after the application of the new method, the intelligent distribution network voltage stability index Vm is always less than 1, and the line stability has been significantly improved.

Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm

A novel wind turbine direct driving heat pump (WTDHP) system is proposed, in which the heat pump is employed to replace the wind turbine's generator. The impacts of the blades with different tip speed ratios on WTDHP system are analyzed in this study. The inverse design method, verified by importing the design shape into FAST for simulation and comparing the simulated tip speed ratio and axial induction factor, was employed to create blades with different tip speed ratios. The heating performances of different blade shape designs at various turbulence and wind speeds are understood. The conclusion is drawn that the blades with a low tip speed ratio (λ=6–8) are clearly superior for heating in the turbulence categories IEC A at wind speeds between 6 and 14 m/s. However, the dynamic responses of the system are weakened by low turbulence and wind speeds. Taking into account the cost and manufacturing process, the blades with a high tip speed ratio can be chosen. Furthermore, the multi-objective optimization of blades was carried out using the enhanced Particle Swarm Optimization (PSO) algorithm, considering heating capacity, flapping load and blade mass. Based on the Wilson method, initial blade of PSO optimization algorithm is generated. Obtaining the heating capacity, flapping load and blade mass in real time from the simulation, the objective functions is calculated to update the status of particle swarm. After optimization using the improved multi-objective algorithm under turbulent wind with an average wind speed of 7 m/s, the blade mass is reduced by 2.96%, the flapping moment is reduced by 3.17%, and the heating capacity is increased by 1.22%. The results show that the optimization effect is more obvious at higher wind speeds.

Optimization of integrated energy system considering multi-energy collaboration in carbon-free hydrogen port

Renewable energy is highly efficient, clean, and low-carbon, and it has become the key to energy transformation. The lack of renewable energy consumption capacity has become a major restriction on the development of renewable energy generation industry, and the application of hydrogen storage technology to port integrated energy systems (IES) is considered an effective solution to the problem of grid-connection of renewable energies. The application of hydrogen storage technology to improve renewable energy consumption and integrated energy use has important research significance. This paper studies optimization of the IES considering multi-energy collaboration in carbon-free hydrogen ports. Security constrained unit commitment (SCUC) is a key issue in the operation of IES. A mixed integer linear programming (MILP) model is constructed with objectives of minimizing the operating cost of energy system. Furthermore, a customized enhanced particle swarm optimization (PSO) method is designed to solve the SCUC optimization problem. Enhanced PSO can obtain satisfying solutions in large-scale instances within a short time. Extensive numerical experiments have been conducted and the results demonstrate the applicability and efficiency of enhanced PSO in solving problems.

Detection of Brain Cancer through Enhanced Particle Swarm Optimization in Artificial Intelligence Approach

Brain cancer is deadly and requires prompt detection and treatment. We propose a complete brain cancer detection method using binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization (PSO), wavelet transform, and neural networks. First, binary encoding converts categorical patient data and medical history information into binary vectors for fast analysis. Adaptive thresholding then handles image lighting and contrast to optimize brain image segmentation. Brain tumor boundaries are identified via edge-based segmentation. This method isolates tumor areas for investigation by recognizing significant pixel intensities. Particle swarm optimization optimizes segmentation algorithm settings, enhancing efficiency and accuracy. Wavelet transform captures local and global brain picture changes, extracting tumor-related information. This method gives a complete visual representation, improving categorization. Finally, utilizing the collected attributes, a neural network model classifies brain pictures as malignant or non-cancerous. The neural network learns the complicated correlations between retrieved variables and brain cancer to classify accurately and automatically. A dataset of brain pictures, comprising malignant and non-cancerous instances, evaluates the proposed approach. The proposed approach accurately detects brain tumors in experiments. Binary encoding, adaptive thresholding, edge-based segmentation, particle swarm optimization, wavelet transform, and neural networks can help medical professionals diagnose and treat brain cancer early.

A multimaterial sensor with Janus characteristics based on enhanced particle swarm optimization algorithm

This paper presents an enhanced particle swarm optimization (EPSO) algorithm for optimizing the parameters of a layered photonic structure. By dynamically adjusting the thickness and refractive index of each dielectric layer, the phase and absorption properties of the transmitted electromagnetic waves (EWs) can be modified, effectively achieving high absorption rates by incorporating defects formed by the Kerr nonlinear material. The proposed EPSO algorithm facilitates the identification of the global optimal solution, resulting in the formation of sharp absorption peaks with values exceeding 0.9 for incident EWs from various directions. Leveraging the Janus properties, the EW exhibits distinct physical characteristics when incident from the front and back directions, enabling the creation of sensors tailored for thickness, angle, and pressure sensing. The forward angle sensing demonstrates a high sensitivity (S) of 0.32 nm/degree, a quality factor (Q) of 543.9, and a figure of merit (FOM) of 0.32 degree−1. Both pressure and thickness can be sensed in both forward and backward directions, with S of 5.46 nm/GPa and 10.24 nm/GPa, mean Q of 563.9 and 316.7, and FOM of 5.5 GPa−1 and 3.3 GPa−1, respectively. Thus, it can be stated that the proposed EPSO holds promising prospects for optical sensor design.

Optimal capacitor planning for power factor improvement using hybrid particle swarm and harmony search optimization

Industrial loads reduce the Power Factor (PF) of supply systems, causing increases in power losses, damaging equipment and higher utility bills. Optimization techniques are used in planning reactive sources to improve PF of power systems. However, conventional techniques suffer difficulties in passing over local optimal, divergence risk, constraints handling or computing higher order derivatives. Herein, the hybridization of Particle Swarm and Harmony Search Algorithm (PS – HSA) is developed for optimal capacitor planning to improve PF, and comparison is made with the Enhanced Particle Swarm Optimization (EPSO) and Improved Adaptive Harmony Search Algorithm (IAHSA). The test systems are the Modified IEEE 6 and 16 buses and nodes respectively. To create semblance of industrial load dominated power systems, the test networks were modified by increasing the reactive load demand at all buses of the IEEE 6 and 16 by 50% and 70% respectively. The capacitor is modelled as static shunt-controlled element deployed to inject reactive power at buses/nodes. Results show that for IEEE 6 buses, PF improved from 0.68 to 0.8983, 0.8986 and 0.8992 with EPSO, IAHSA and hybrid PS – HSA respectively. Similarly, in IEEE 16 nodes, PF improved from 0.76 to 0.9439, 0.943, and 0.944 with EPSO, IAHSA and hybrid PS – HSA respectively. Furthermore, real power losses reduced from 16.94 MW to 14.03 MW in IEEE 6 buses, translating to 17.2% reduction with the hybrid PS - HSA. While in IEEE 16 nodes, reduction is from 0.719 MW to 0.69 MW accounting for 4% reduction, also with the hybrid PS - HSA.

Enhancing Security in Mobile Ad Hoc Networks: Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm

Wireless technologies have grown in popularity and are used in many applications. Transient Mobile Ad hoc Networks (MANETs) serve specific goals without infrastructure. The dynamism of these networks makes them useful for ubiquitous computing. However, high mobility, the lack of a centralized authority, and open media make MANETs vulnerable to various security risks. Thus, an Intrusion Detection System (IDS) should be used to monitor and detect system security issues. To prevent and improve security against unauthorized access, intrusion screening is crucial. The depletion of a mobile node's power supply can impact its capacity to transmit packets, as this functionality is contingent upon the system's overall lifespan. Computational Optimization-driven solutions have been prevalent in the context of IDS and secure routing inside MANETs. This research employs the Enhanced Particle Swarm Optimization-driven Intrusion Detection and Secure Routing Algorithm (EPSO-IDSRA). Enhanced Particle Swarm Optimization technique (EPSO) has provided confidence-based, secure, and energy-efficient routing in MANETs. The EPSO technique is applied to identify optimal hops for enhancing the routing process. The initial step involves the activation of the fuzzy clustering algorithm, followed by selecting Cluster Heads (CHs) based on assessing their indirect, direct, and recent confidence values. Furthermore, the identification of value nodes was contingent upon assessing confidence levels. Also, the CHs are involved in multi-hop routing, and determining the optimal route depends on the anticipated protocol, which chooses the most favorable paths considering factors such as latency, throughput, and connectivity within the designated area. The EPSO method, presented for secure routing (at time 50ms), yielded an optimal energy consumption of 0.15 millijoules, a minimal delay of 0.008 milliseconds, a maximum throughput of 0.8 bits per second, and an 89% detection rate.

Reactive Power Optimization of Distributed PVConnected to Three-Phase Unbalanced Distribution Network

This study presents an approach to optimize reactive power in three-phase unbalanced distribution networks using an enhanced particle swarm optimization algorithm. The study analyzes the characteristics of reactive power optimization and establishes a reactive power optimization model for the distribution network using a multi-objective optimization method. Inequality constraints are utilized to determine the reactive power optimization model constraints for three-phase unbalanced distribution networks. The study applies an improved particle swarm optimization algorithm to solve the reactive power model and achieve optimization. The experimental results demonstrate that the proposed method considerably reduces the unused power of the optimized power network within 0-500ms and achieves a maximum reactive power optimization time of fewer than 8.2s, thereby enhancing the efficiency of reactive power optimization in distribution networks.

Minimization of Active Power Loss Using Enhanced Particle Swarm Optimization

Identifying the weak buses in power system networks is crucial for planning and operation since most generators operate close to their operating limits, resulting in generator failures. This work aims to identify the critical/weak node and reduce the system’s power loss. The line stability index (Lmn) and fast voltage stability index (FVSI) were used to identify the critical node and lines close to instability in the power system networks. Enhanced particle swarm optimization (EPSO) was chosen because of its ability to communicate with better individuals, making it more efficient to obtain a prominent solution. EPSO and other PSO variants minimized the system’s actual/real losses. Nodes 8 and 14 were identified as the critical nodes of the IEEE 9 and 14 bus systems, respectively. The power loss of the IEEE 9 bus system was reduced from 9.842 MW to 7.543 MW, and for the IEEE 14 bus system, the loss was reduced from 13.775 MW of the base case to 12.253 MW for EPSO. EPSO gives a better active power loss reduction and improves the node’s voltage profile than other PSO variants and algorithms in the literature. This suggests the feasibility and suitability of EPSO to improve the grid voltage quality.

Production analytics for renewable energy portfolio management: A comparative study of active and passive strategies in digital twin

Developed societies are facing a number of problems due to technological advances, including the increased requirement for power, resulting in small electrical grids utilizing local power sources, also known as a microgrid (MG). The MG is linked to the central electrical network or runs alone in the form of a power island based on the renewable energy sources and dispatchable units. On the other hand, the smart grid concept is built based on the employment of sensors and cloud computing systems to address the problems. It is possible to decrease energy consumption problems by using a smart MG; however, proper implementation is necessary to avoid generating too much information. As a result, the study uses fog computing for energy production and consumption analysis and management by reducing the amount of information passing to the cloud. The proposed model would reduce service response times in an active strategy. To this end, an enhanced particle swarm optimization with Levy Walk is used for solving the optimization problem based on the real-time data receiving from the digital twin of the system. As a result, to evaluate the efficiency and accuracy of the fog in the case, efficiency evaluations based on active and passive strategies are carried out in order to determine whether parameters including the number of residents, the optimization algorithm, moving appliances, and the power resource affect response times and source demands.

An optimal management architecture based on digital twin for smart solar-based islands incorporating deep learning and modified particle swarm optimization

Smart island (SI) energy management is a type of energy management system used to ensure that energy is used efficiently on islands. It is designed to reduce energy consumption and costs while also improving the sustainability of the island. Smart island energy management systems use a combination of technologies such as solar, wind, and other renewable energy sources, energy storage systems, smart meters, and advanced analytics to monitor and manage energy usage. The system can be used to provide islanders with real-time energy usage data, allowing them to make informed decisions about their energy use. Using the effects on the environment and economics of the SI as a basis for fully modeling the optimum performance of the SI in grid-connected operations, the feasibility and balance of the SI power grid are ensured. In addition, taking into account the impact of interruptible loads (IL) on SI operation costs, a power shift for IL can be performed using storage batteries in a digital twin environment. A new ecology-driven optimization algorithm has been developed that continuously adjusts migration rates with habitat suitability indexes of normalized individuals and adds a differential perturbation to the migration operator of the migration process. The enhanced particle swarm optimization algorithm has been implemented as the SI optimization dispatching algorithm. In order to have a precise prediction of the renewable energy sources, their output power is predicted using the recurrent neural network (RNN) deep learning model. Based on the simulation outcomes, it is evident that the suggested power dispatching model could greatly decrease the overall price of the system by implementing the advanced and effective algorithm and model presented in the study.