Multi-swarm Optimization Research Articles

IMPROVE OF UNCERTAIN MICROSATELLITE MAGNETIC CLEANLINESS BASED ON MAGNETIC FIELD SPATIAL HARMONICS COMPENSATION

Problem of microsatellite magnetic cleanliness (MMC) improving by magnetic field (MF) spatial harmonics compensa-tion and magnetic characteristics uncertainty (MCU) sensitivity reducing considered. Prediction and control by uncer-tain microsatellite MC design are geometric inverse magneto static problem (GIMSP) reduced to vector game solution. Vector payoff calculated based on development method for analytical calculation of magnetostatic field induction of spherical sources in the Cartesian coordinate system (CCS) using Wolfram Mathematica ® software. Both vector game solution calculated based on particles multi-swarm optimization (PMSO) algorithms from Pareto optimal solutions tak-ing into account binary preference relations. Prediction model and location of compensating units in spherical coordi-nates as well as multipole harmonic coefficients of dipoles, quadrupoles and octupoles are calculated during prediction and control of uncertain microsatellite MC. Results of MC improving for microsatellite «Sich» family by compensation of dipoles, quadrupoles and octupoles components of initial MF spatial harmonics and reducing sensitivity to MCU are given. References 17, figures 2.

Automated packing and piping in an Airbus A320 main landing gear bay: an industrial development case study

The packing of given hydraulic equipment together with the hydraulic piping is a challenging design task. Both the hydraulic domain and the geometry domain are highly intercoupled as the packing determines the exact location of the connection points and tangents for the pipes but also defines the installation spaces for the equipment to be positioned which needs to be taken into account as an obstacle during the piping process. For the industrial case study, a mounting rack in an Airbus A320 main landing gear bay is used, where seven pieces of equipment should be packed and 22 pipes should be routed. In the first part of the study, the packing was done manually, assisted by automated routing, while the hydraulic piping was generated automatically. In the second part of the study the packing was automatized as well. For the automated packing process, a particle multi swarm optimisation was used. As objective function a pipe length minimization was implemented, which uses the Dubins Path as a pipe approximation. Constraints taken into account are the collision-free nature of the equipment and its placement on a plate inside the plates borders. The automated packing workflow was used to generate different variants of which for two a pipe work was generated exemplarily. Due to the model-based systems engineering approach, no further interaction in terms of the definition of the connection points, tangents and the installation space is necessary. This demonstrates that an automated packing and piping process in an industrial context using industrial geometries is now feasible.

Harnessing collaborative learning automata to guide multi-objective optimization based inverse analysis for structural damage identification

Structural damage identification based on physical models is often transformed into an optimization problem that minimizes the difference between measurement information of structure being monitored and the model prediction in the parametric space. However, the objective function in this context often exhibits multimodality, involving high-dimensional variables due to the reliance on finite element models for damage identification. These features pose challenges to optimization algorithms, where entrapment in local solutions can lead to false positives and false negatives in damage identification. In this research, we propose a reinforcement learning based multi-swarm optimizer to tackle such challenges in pursuit of a small yet diverse solution set that can capture the true damage scenario as one of the solutions. The proposed method leverages the flexibility of the particle swarm optimizer and incorporates novel strategies of metaheuristics to realize targeted improvement. To enable the particle swarm to adaptively select the appropriate search strategy based on the current environment, we adopt the learning automata technique, which sidesteps the need for reward strategy selection that is usually ad hoc at each step of the search. The integration harnesses the automatic learning and self-adaptation capabilities of learning automata, enabling the particles to navigate based on environmental signals. This leads to accumulated probabilities tied to advantageous movements, fostering an adaptive exploration of particles in the search space. The proposed approach is first validated through implementing into benchmark test cases with comparisons. It is then applied to structural damage identification with piezoelectric admittance experimental signals. `The results highlight the capability of the algorithm to identify a small solution set with high accuracy to match the actual damage scenario.

The method for design of combined electromagnetic shield for overhead power lines magnetic field

Aim. Development of the method of designing a combined electromagnetic shield, consisting of active and passive parts, to improve the effectiveness of reduction of industrial frequency magnetic field created by two-circuit overhead power lines in residential buildings. Methodology. The problem of design of combined electromagnetic shield including robust system of active shielding and electromagnetic passive shield of initial magnetic field solved based on of the multi-criteria two-player antagonistic game. The game payoff vector calculated based on the finite element calculations system COMSOL Muliphysics. The game solution calculated based on the particles multiswarm optimization algorithms. During the design of combined electromagnetic shields spatial location coordinates of shielding winding, the currents and phases in the shielding winding of active shielding, geometric dimensions and thickness of the electromagnetic passive shield are calculated. Results. The results of theoretical and experimental studies of combined electromagnetic passive and active shielding of magnetic field in residential building from power transmission line with a «Barrel» type arrangement of wires presented. Originality. For the first time the method of designing a combined electromagnetic shield, consisting of active and passive parts, for more effective reduction of the magnetic field of industrial frequency created by two-circuit overhead power lines in residential buildings is developed. Practical value. Based on results of calculated and experimental study the shielding efficiency of the initial magnetic field determined that shielding factors whith only electromagnetic passive shield is more 2 units, whith only active shield is more 4 units and with combined electromagnetic passive and active shield is more 10 units. It is shown the possibility to reduce the level of magnetic field induction in residential building from power transmission line with a «Barrel» type arrangement of wires by means of a combined electromagnetic passive and active shielding with single compensating winding to 0.5 μT level safe for the population. References 53, figures 15.

A multi-swarm optimizer with a reinforcement learning mechanism for large-scale optimization

Large-scale global optimization (LSGO) problems involve numerous decision variables, are similar to real-world problems, and have generated research interest. To solve LSGO, a particle swarm optimizer (PSO) has been used. However, the many local optima and huge search space severely limit the effectiveness of the classic PSO. Dealing with the complexity of LSGO while avoiding the local optima is the main challenge of large-scale optimization algorithms. A multiswarm strategy has also been introduced to improve swarm diversity; however, it reduces the convergence speed. Previous studies have shown that reinforcement learning (RL) can improve the convergence ability of EAs owing to its increasing learning ability. In this study, we develop a multiswarm optimizer with an RL mechanism (MSORL) for LSGO. The MSORL includes a tri-particle group structure for subswarms to save the computational cost and balance the diversity and convergence. An RL-guided updating strategy is designed to enhance the convergence speed, and an adaptive tolerance-based search mechanism is employed to improve the diversity and avoid the local optima. The experimental results prove that the MSORL outperforms other state-of-the-art algorithms in terms of the convergence accuracy and speed.

A Multi-Swarm Optimization Using Diverse Charged Particles for Visual Tracking via Template Matching

The visual tracking problem can be solved by performing template matching on each frame, which can be treated as a dynamic optimization problem. On the other hand, Charged Particle Swarm Optimization (CPSO) is a PSO variant for dynamic optimization, which maintains swarm diversity by introducing charged particles and repulsion vectors into the velocity update step. CPSO uses a unified pre-determined amount of charge for all particles, which is difficult to deal with various moves of the optimal solution. Instead of a unified value, in this paper, we propose introducing diverse charged particles into CPSO, referred to as Multi-swarm CPSO (MCPSO). Specifically, MCPSO employs multiple sub-swarms and assigns different amounts of charge to particles in each sub-swarm, which leads to diverse moving characteristics of particles. Moreover, each particle is limited to receiving repulsion force only from other particles in the same sub-swarm, which results in various sub-swarms with different behaviors. The optimization performance of MCPSO is assessed by a numerical experiment using five benchmark functions compared with standard PSO and CPSO. In addition, the effectiveness of MCPSO for visual tracking using template matching is verified by a comparison experiment with nine synthetic sequences.

Rooster Colony Algorithm: A two-step Multi-Swarm Optimization Approach

Particle Swarm Optimization is a metaheuristic optimization algorithm inspired by the collective behavior of animal swarms where a set of candidate solutions, called particles, are randomly initialized in the search space, and their movements are iteratively updated based on their individual best solutions and the global best solution found by the swarm. This paper proposes a Multi-Swarm rooster colony algorithm (RCA) that considers a set of roosters, each owning a group of hens to compose a team. Each team (rooster and its hens) competes for the resource (food) with the other teams. From the combinatorial optimization point of view, each team analyzes part of the search space by an independent PSO algorithm with the same objective function. The RCA algorithm concurrently executes all PSO algorithms with different inertial weights for exploring different regions and the best solution (Gbest) of each team will compose the initial population for a new further centralized PSO algorithm that will exploit the previous solutions to search for the optimal one. Thus, the proposed RCA is composed of two steps, based on exploration and exploitation strategies to find an optimized solution in the search space. The results show that the proposed algorithm is competitive in solving well-known optimization functions. The objective is to apply this technique to solving real-life scheduling problems.

Multi-modal multi-objective particle swarm optimization with self-adjusting strategy

Since the exploration of multiple solution sets will lead to the deterioration of convergence in multi-objective particle swarm optimization, the motion of the particles is severely disturbed by the under-convergence solutions in multi-modal multi-objective optimization problems (MMOPs). To solve this problem, a multi-modal multi-objective particle swarm optimization with self-adjusting strategy (MMOPSOSS) is proposed to promote the complete convergence of multiple solution sets through the self-adjusting of parameters and population size. First, a multi-swarm optimization framework is designed to obtain diverse convergence directions. Second, a self-adjusting local search mechanism is introduced to improve the search performance of sub-swarms in the potential regions according to the feedback information detected by diversity entropy under this framework. Third, a sub-swarm-balancing strategy is developed to balance the degree of convergence among different regions by adjusting the size of the sub-swarms. Finally, MMOPSOSS is compared with several multi-modal multi-objective optimization algorithms in benchmark experiments and engineering simulation experiments. The results demonstrate that MMOPSOSS has a positive effect on the convergence of multiple solution sets for MMOPs.

Energy-Efficient Clustering Using Optimization with Locust Game Theory

Wireless sensor networks (WSNs) are made up of several sensors located in a specific area and powered by a finite amount of energy to gather environmental data. WSNs use sensor nodes (SNs) to collect and transmit data. However, the power supplied by the sensor network is restricted. Thus, SNs must store energy as often as to extend the lifespan of the network. In the proposed study, effective clustering and longer network lifetimes are achieved using multi-swarm optimization (MSO) and game theory based on locust search (LS-II). In this research, MSO is used to improve the optimum routing, while the LS-II approach is employed to specify the number of cluster heads (CHs) and select the best ones. After the CHs are identified, the other sensor components are allocated to the closest CHs to them. A game theory-based energy-efficient clustering approach is applied to WSNs. Here each SN is considered a player in the game. The SN can implement beneficial methods for itself depending on the length of the idle listening time in the active phase and then determine to choose whether or not to rest. The proposed multi-swarm with energy-efficient game theory on locust search (MSGE-LS) efficiently selects CHs, minimizes energy consumption, and improves the lifetime of networks. The findings of this study indicate that the proposed MSGE-LS is an effective method because its result proves that it increases the number of clusters, average energy consumption, lifespan extension, reduction in average packet loss, and end-to-end delay.

An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the surroundings. Designing energy-efficient data gathering methods in large-scale Wireless Sensor Networks (WSN) is one of the most difficult areas of study. As every sensor node has a finite amount of energy. Battery power is the most significant source in the WSN. Clustering is a well-known technique for enhancing the power feature in WSN. In the proposed method multi-Swarm optimization based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing optimization. By using distributed data transmission modification, an adaptive hierarchical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire area. To begin, a hierarchical clustering-based routing protocol is presented in terms of balancing node energy consumption. The Multi-Swarm optimization (MSO) based Genetic Algorithms are proposed to select an efficient Cluster Head (CH). It also improves the network’s longevity and optimizes the routing. As a result of the study’s findings, the proposed MSO-Genetic Algorithm with Hill climbing (GAHC) is effective, as it increases the number of clusters created, average energy expended, lifespan computation reduces average packet loss, and end-to-end delay.

Dynamic Interconnection Approach With BLX-Based Search Applied to Multi-Swarm Optimizer: An Empirical Analysis to Real Constrained Optimization

The Multi-Swarm approach allows the use of multiple configurations between two or more populations of particles, where each one can present different approaches (e.g. lbest, gbest, Unified, Guaranteed-Convergence) directed towards improving the optimization process. This article presents a proposal for local/global stochastic interconnection applied to the context of the Multi-Swarm algorithm, as well as for incrementing a local search method for refining previously obtained solutions. Two proposals are introduced for this new Multi-Swarm PSO (MSO). The first one is the inclusion of “counterpart particles”, which establishes a sub-topology between inter-swarm particles, accessed by migration and evaluability rules. The other involves using customized crossover operators and is based on the BLX scheme (Blend Crossover) with direction information used as a reference for establish a subspace search around the particles. Performance and robustness of the new approaches were assessed by ten constrained engineering design optimization problems (COPs), as is compared to other solutions already published in the scientific literature. Results indicate significant performance improvements for all 10 COPs when compared to concurrent-based MSOs. By making available new references from other swarms, the counterpart particles approach tends to improve the optimization process in the search space, while an intermediate layer of local search based on a modified directed BLX crossover should provide an extra search around the particle, and thus, refining previously obtained solutions.

Design and Optimization of Fuzzy-Based FIR Filters for Noise Reduction in ECG Signals Using Neural Network

Cardiovascular disease (CVD) has been identified as a threat to human life for decades, with the majority of individuals dying as a result of delayed diagnosis and treatment. An electrocardiogram (ECG) plays a vital role in the prognosis of such an ailment. The presence of noise and artifacts complicates the accurate detection and identification of CVD. As a result, reliable signal recovery tasks necessitate noise removal, which is an inverse problem. The main noises present in electrocardiogram (ECG) signals are EMG noise, electrode motion artifact noise. In this paper, radial basis function (RBF) and multi swarm optimization neural network (MSONN) are used to denoise the ECG signal. The cut-off frequency is calculated using a low-pass filter. By using, fuzzy FIR filtering technique baseline wander noises can be removed. Results show that MOS based approach outperforms existing approaches in terms of accuracy and is observed to be 87% even when the dataset size is small. Further, noises if any exists are also removed by the use of cascaded multiplier less Fuzzy FIR filters

Efficient DNA-ligand interaction framework using fuzzy C-means clustering based glowworm swarm optimization (FCMGSO) method

Assessment of DNA and ligand interaction is a great challenge to the medical researchers and drug industries since the accurate mapping of DNA and ligand plays an important role in associating drugs for suitable diseases. The primary objective of this research work is to develop an efficient model for predicting the best DNA and Ligand mapping. In this research work, 500 instances of DNA and drugs used for cancer and non-cancer diseases from the National Centre for Biotechnology Information (NCBI) were considered for analysis. Binding energy is one of the important measures to predict and finalize the best DNA and ligand interaction. Existing methods used for the docking process such as Simulated Annealing (SA), Lamarckian Genetic Algorithm (LGA), Genetic Clustering (GC), Fuzzy C-means clustering (FCM), and Genetic Clustering with Multi swarm Optimization (GCMSO) were applied for all 500 instances. These algorithms failed to produce better binding energy due to a lack of optimization in the existing approaches. Optimization methods play a major role in predicting accurate DNA ligand docking. Hence, this research proposes an efficient architecture using Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) optimization method for accurate analysis of the DNA-ligand docking process. Results are proving that the proposed FCMGSO algorithm shows less binding energy than other existing methods in all instances of samples considered from the NCBI dataset. Communicated by Ramaswamy H. Sarma

Using machine learning algorithm for detection of cyber-attacks in cyber physical systems

Network integration is common in cyber-physical systems (CPS) to allow for remote access, surveillance, and analysis. They have been exposed to cyberattacks because of their integration with an insecure network. In the event of a violation in internet security, an attacker was able to interfere with the system's functions, which might result in catastrophic consequences. As a result, detecting breaches into mission-critical CPS is a top priority. Detecting assaults on CPSs, which are increasingly being targeted by cyber criminals and cyber threats, is becoming increasingly difficult. Machine Learning (ML) and Artificial Intelligence (AI) have the potential to make these the worst of moments, but it may also be the finest of times. There are a variety of ways in which AI technology can aid in the growth and profitability of a variety of industries. Such data can be parsed using ML and AI approaches in designed to check attacks on CPSs. Hence, in this paper, we propose a novel cyberattack detection framework by integrating AI and ML (ML) methods. Here, initially we collect the dataset from the CPS database and preprocess the data using normalization for removal of errors and redundant data. The features are extracted using Linear Discriminant Analysis (LDA). We have proposed Self-tuned Fuzzy Logic-based Hidden Markov Model (SFL-HMM) with Heuristic Multi-Swarm Optimization (HMS-ACO) algorithm for detection of the cyberattacks. The proposed method is evaluated using the MATLAB simulation tool and the metrics are compared with existing approaches. The results of the experiments reveal that the framework is more successful than traditional strategies in achieving high degrees of privacy. Furthermore, in terms of detection rate, false positive rate, and computing time, the framework beats traditional detection algorithms.

Multi-Objective Optimization Using Cooperative Garden Balsam Optimization with Multiple Populations

Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve multi-objective optimization, applying multiple populations for multiple objectives individually. Moreover, in order to improve the diversity of the solutions, both crowding distance computations and epsilon dominance relations are adopted when updating the archive. Furthermore, an efficient selection procedure called co-evolutionary multi-swarm garden balsam optimization (CMGBO) is proposed to ensure the convergence of well-diversified Pareto regions. The performance of the used algorithm is validated on 12 test functions. The algorithm is employed to solve four real-world problems in engineering. The achieved consequences corroborate the advantage of the proposed algorithm with regard to convergence and diversity.

Improving the Quantum Multi-Swarm Optimization with Adaptive Differential Evolution for Dynamic Environments

In this study, the modification of the quantum multi-swarm optimization algorithm is proposed for dynamic optimization problems. The modification implies using the search operators from differential evolution algorithm with a certain probability within particle swarm optimization to improve the algorithm’s search capabilities in dynamically changing environments. For algorithm testing, the Generalized Moving Peaks Benchmark was used. The experiments were performed for four benchmark settings, and the sensitivity analysis to the main parameters of algorithms is performed. It is shown that applying the mutation operator from differential evolution to the personal best positions of the particles allows for improving the algorithm performance.

Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network.

Wireless Sensor Networks (WSNs) can be defined as a cluster of sensors with a restricted power supply deployed in a specific area to gather environmental data. One of the most challenging areas of research is to design energy-efficient data gathering algorithms in large-scale WSNs, as each sensor node, in general, has limited energy resources. Literature review shows that with regards to energy saving, clustering-based techniques for data gathering are quite effective. Moreover, cluster head (CH) optimization is a non-deterministic polynomial (NP) hard problem. Both the lifespan of the network and its energy efficiency are improved by choosing the optimal path in routing. The technique put forth in this paper is based on multi swarm optimization (MSO) (i.e., multi-PSO) together with Tabu search (TS) techniques. Efficient CHs are chosen by the proposed system, which increases the optimization of routing and life of the network. The obtained results show that the MSO-Tabu approach has a 14%, 5%, 11%, and 4% higher number of clusters and a 20%, 6%, 14%, and 6% lesser average packet loss rate as compared to a genetic algorithm (GA), differential evolution (DE), Tabu, and MSO based clustering, respectively. Moreover, the MSO-Tabu approach has 136%, 36%, 136%, and 38% higher lifetime computation, and 22%, 16%, 51%, and 12% higher average dissipated energy. Thus, the study’s outcome shows that the proposed MSO-Tabu is efficient, as it enhances the number of clusters formed, average energy dissipated, lifetime computation, and there is a decrease in mean packet loss and end-to-end delay.

Method of adjustment of three circuit system of active shielding of magnetic field in multi-storey buildings from overhead power lines with wires triangular arrangement

Aim. For the first time the method of adjustment of three circuit system of the active shielding of the magnetic field based on experimentally determined space-time characteristics to increase the shielding factor in a multi-storey building located near a single-circuit overhead transmission lines with a wires triangular arrangement was developed. Methodology. When synthesizing the laboratory model of system of active shielding the coordinates of spatial arrangement and of three shielding coils, the currents in shielding coils and resulting magnetic flux density value in the shielding space were calculated. The synthesis is based on the multi-criteria game decision, in which the payoff vector is calculated on the basis on quasi-stationary approximation solutions of the Maxwell equations. The game decision is calculated based on the stochastic particles multi swarm optimization algorithms. Results. Computer simulation and experimental research of the space-time characteristics of laboratory model of three circuit system of active shielding of magnetic field, generated by overhead power lines with phase conductors triangle arrangements in multi-storey building are given. The possibility of initial magnetic flux density level reducing to the sanitary standards level is shown. Originality. For the first time the synthesis and adjustment of laboratory model of three circuit system of active shielding of magnetic field based on experimentally determined space-time characteristics to increase the shielding factor in a multi-storey building located near a single-circuit overhead transmission lines with a wires triangular arrangement carried out. Practical value. Practical recommendations from the point of view of the implementation of developed method of adjustment of the three circuit system of the active shielding of the magnetic field based on experimentally determined space-time characteristics in a multi-storey building located near a single-circuit overhead transmission lines with a triangular arrangement of wires are given.

Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services

Multi-swarm optimization model for multi-cloud scheduling for enhanced quality of services

Overhead power lines magnetic field reducing in multi-story building by active shielding means

Aim. Reducing of magnetic flux density of magnetic field in multi-storey building, generated by overhead power lines to the sanitary standards level by active shielding means. The tasks of the work are the synthesis, computer simulation and experimental research of three-circuits system of active shielding, which includes three shielding coils. Methodology. When synthesizing the system of active shielding of magnetic field, are determined their number, configuration, spatial arrangement and of shielding coils as well as the shielding coils currents and resulting magnetic flux density value in the shielding space. The synthesis is based on the multi-criteria game decision, in which the payoff vector is calculated on the basis on quasi-stationary approximation solutions of the Maxwell equations. The game decision is based on the stochastic particles multiswarm optimization algorithms. Results. Computer simulation and experimental research of three-circuit system of active shielding of magnetic field, generated by overhead power lines with phase conductors triangle arrangements in multi-storey building are given. The possibility of initial magnetic flux density level reducing in multi-storey building to the sanitary standards level is shown. Originality. For the first time to reducing of magnetic flux density of magnetic field in multi-storey building the synthesis, computer simulation and experimental research of three-circuit system of active shielding of magnetic field generated by single-circuit overhead power line with phase conductors triangular arrangements carried out. Practical value. Practical recommendations from the point of view of the practical implementation on reasonable choice of the spatial arrangement of three shielding coils of three-circuit system of active shielding of the magnetic field generated by single-circuit overhead power line with phase conductors triangular arrangements in multi-storey building are given.