Multi-objective Particle Swarm Optimization Research Articles

.A multi-objective search-based approach for position and orientation deviations in assemblies with multiple non-ideal surfaces

Computing position and orientation deviations (PODs) considering non-ideal morphologies and local deformations of mating surfaces is a key challenge in controlling the performances of assembled mechanical products through tolerance analysis. However, existing computational models based on Skin Model Shapes face difficulty in handling the PODs for all types of assembly issues. This paper proposes a novel multi-objective search-based approach for POD computation on a conception of force equilibrium constraints. A multi-objective particle swarm optimization algorithm is employed to solve the multi-objective model, and the efficiency and accuracy of the proposed approach are tested and compared with the NSGA-II and FEM methods. To demonstrate its distinctive capability to deal with assembly issues with multiple non-ideal mating surfaces, the proposed approach is applied to assemblies with multiple planar and cylindrical surfaces. Finally, an application of predicting the coaxial accuracy of a fuel pump is carried out, and the capabilities of the proposed approach are further demonstrated.

Modeling Thermal Impedance of IGBT Devices Based on Fractional Calculus Techniques

The thermal impedance characteristics of insulated gate bipolar transistor (IGBT) modules are critical for the thermal management and design of electronic devices. This paper proposes a fractional-order equivalent thermal impedance model, which is inspired by the correlation between multi-time-scale dissipation characteristics of heat conduction processes and fractional calculus. The fractional-order equivalent thermal impedance model is derived based on the connection between fractional-order calculus and the Foster thermal network model in mathematical operations, with only two parameters to be identified: heat capacity C and fractional order α. Moreover, this paper provides a parameter identification method for the proposed fractional-order equivalent thermal impedance model based on the multi-objective particle swarm optimization (MOPSO) algorithm. In order to validate the effectiveness and superiority of this work, experiments and comparative works are provided in this paper. The results indicate that the fractional-order equivalent thermal impedance model can accurately describe the frequency domain characteristic curves of the thermal impedance of the Foster thermal network model for IGBT modules, with the difference between the amplitude frequency characteristics not exceeding 1 dB and the difference between the phase frequency characteristics not exceeding 1° within the operating frequency range of (1 kHz, 1 MHz).

FastSLAM-MO-PSO: A Robust Method for Simultaneous Localization and Mapping in Mobile Robots Navigating Unknown Environments

In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings.

Techno-economic assessment of surfactant Huff-n-Puff EOR in shale plays via multi-objective optimization

Surfactant Huff-n-Puff (HnP) has proven effective for enhanced oil recovery (EOR) in unconventional petroleum reservoirs. Due to the high cost of surfactant injectants, a comprehensive techno-economic analysis is crucial for optimizing development strategies. This study focuses on the Jimusar J305 shale oil reservoir, employing a Pareto-optimal multi-objective optimization (MOO) model to identify the best injection-production strategies for surfactant HnP. Using crude oil recovery and economic performance as objectives, the model combines numerical simulations with the multi-objective particle swarm optimization (MOPSO) algorithm. A decision-making protocol based on Pareto solutions is proposed to balance objectives, alongside an EOR mechanism analysis. Results show the net present value (NPV) improved from −2.98 million to +3.48 million USD without reducing oil recovery, indicating that different surfactant HnP strategies can achieve similar recovery outcomes but vary significantly in profitability. Optimal project design involves adjusting surfactant volume to reverse rock wettability and balancing injection rates and concentrations to enhance production while controlling costs.

Optimal allocation model of port emergency resources based on the improved multi-objective particle swarm algorithm and TOPSIS method

The busy maritime traffic and occurrence of ship accidents have led to a growing recognition of the necessity to maritime emergency resources allocation. The port emergency resource allocation is of significant importance for the maritime safety. This paper presents an optimized allocation model for port emergency resources based on the improved multi-objective particle swarm optimization (IMOPSO). The model introduces the crowding distance and improves the external archive update strategy. The particle inertia weight is adjusted and a dynamic mutation operator is incorporated. The entropy-weighted technique for order preference by similarity to an ideal solution method is also employed to identify the optimal solution. A comprehensive comparison with MOPSO has been presented and discussed. Three metrics of generational distance (GD), spacing (SP) and delta indicator (Δ) were employed for performance evaluation. The results demonstrated that the proposed IMOPSO algorithm exhibited superior performance and robustness, with average values of GD = 0.0386, SP = 0.0023 and Δ = 0.6468 for ZDT test functions. The model efficacy is further validated by a case study of oil spill dispersant configuration at Zhanjiang Port, China. Seven alternative schemes have been obtained, among which the optimal scheme is selected by the entropy-weighted TOPSIS method. The overall cost is potentially to be reduced by approximately 33.03 %. The present study would provide a reference for the water pollutant control and environmental management in port waters.

Inverse-Designed Ultra-Compact Passive Phase Shifters for High-Performance Beam Steering.

Ultra-compact passive phase shifters are inversely designed by the multi-objective particle swarm optimization algorithm. The wavelength-dependent phase difference between two output beams originates from the different distances of the input light passing through the 4 μm × 3.2 μm rectangular waveguide with random-distributed air-hole arrays. As the wavelength changes from 1535 to 1565 nm, a phase difference tuning range of 6.26 rad and 6.95 rad is obtained for TE and TM modes, respectively. Compared with the array waveguide grating counterpart, the phase shifters exhibit higher transmission with a much smaller footprint. By combining the inverse-designed phase shifter and random-grating emitter together, integrated beam-steering structures are built, which show a large scanning range of ±25.47° and ±27.85° in the lateral direction for TE and TM mode, respectively. This work may pave the way for the development of ultra-compact high-performance optical phased array LiDARs.

A Stochastic Sustainable Closed-Loop Supply Chain Networks for Used Solar Photovoltaic Systems: Meta-Heuristic Comparison and Real Case Study

This research presents a novel approach to setting up a sustainable Closed-Loop Supply Chain (CLSC) network for used solar photovoltaic (PV) systems, addressing end-of-life product waste from solar panel installations and manufacturing centers. The model accounts for uncertainties in PV systems and aims to efficiently collect, refurbish, and recycle used solar PV systems, promoting a circular and environmentally responsible waste management strategy. The supply chain network comprises vendors, collection centers, hybrid centers, distribution centers, and manufacturing centers, with objectives to maximize total profit, minimize environmental risk, and maximize service levels by demonstrating the profitable reuse of used solar photovoltaic systems by manufacturers. The epsilon-constraint method is utilized to handle the model's multi-objectiveness and identify Pareto optimal solutions. A case study in Iran is conducted to validate the methodology's performance, comparing results obtained from three meta-heuristic methods: Non-Dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Gray Wolf Optimization (MOGWO). The average error rates are 0.0358 for MOGWO, 0.1248 for MOPSO, and 0.2066 for NSGA-II. Sensitivity analysis highlights the significant impact of demand variations on all objective functions. Lastly, the numerical results are discussed to provide managerial insights for informed decision-making.

Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization

This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.

A multi-objective indirect neural adaptive processes control design for minimization of energy consumption: An experimental validation on a transesterification reactor

Nowadays, energy management environment is a very important issue that technologies have focused on in order to save costs and minimize energy waste. This objective can be achieved by means of an energy resource management approach through an appropriate optimization technique. However, energy savings can conflict with other objective functions, to solve the problem a multi-objective optimization that considers the minimization of the control energy can be adopted. In this paper, we propose to use a multi-objective indirect neural adaptive control concept for nonlinear systems with unknown dynamics. The control scheme consists of an adaptive instantaneous neural emulator and neural controller built on fully connected real-time recurrent learning networks (RTRL). The multi-objective particle swarm optimization (MOPSO) algorithm is used as a mechanism to find NE and NC adaptive learning rates that optimize the proposed objective functions: control energy minimization and closed-loop preservation. Comparative studies and experimental validation on a semi-batch reactor, used to generate cleaner biofuels, are performed to confirm the effectiveness of the proposed strategy.

Optimizing urban flood management: enhancing urban drainage system efficiency under extreme rainfall events

ABSTRACT Extreme rainfall events, particularly those induced by tropical cyclones, pose a heightened risk to the urban drainage system (UDS). Existing UDSs, having been established long ago, often fail to account for the extreme rainfall caused by cyclones. To address this issue, this study designs a multi-objective intelligent scheduling model within a simulation -optimization framework, aiming to optimize the operation of urban drainage infrastructure and hydraulic structures. This is achieved by integrating the Storm Water Management Model (SWMM) with the multi-objective particle swarm optimization algorithm (MOPSO) and distinctly evaluating typhoons and torrential rains for their impact on extreme rainfall. The study results show that the multi-objective intelligent scheduling model can effectively devise operation strategies for pumping stations and weirs in the study area, thereby optimizing their use for urban drainage. The model was successful in reducing the total flood volume (TFV) and the water level fluctuation (WLF) by 3.11%–57.77% and 26.32%–65.48%, respectively. This not only mitigates urban flooding but also enhances the infrastructure stability of the UDS. The model outperformed the local adaptation strategy in most scenarios for the two selected objectives, suggesting that the efficiency can be significantly improved by optimizing UDSs without expansion of existing infrastructure or additional costs.

Optimization for fuel consumption and TCO of a heavy-duty truck with electricity-propelled trailer

As renewable energy grows in the heavy-duty truck sector, exploration of diverse solutions with varied energy types and powertrain configurations becomes a necessity. While most studies focus on powertrain configurations in tractors, this study takes a different approach: propose and evaluate new plug-in hybrid configurations with multiple power sources for heavy-duty trucks. The tractor retains its engine powertrain, while the trailer is equipped with electric axle(s), forming a fuel-electricity hybrid propulsion system. The specifications of the electric propulsion system are optimized, using a multi-objective particle swarm optimization algorithm and dynamic programming control algorithm to evaluate the energy consumption and total cost of ownership (TCO). The results highlight the potential of the configuration with three electric axles and single reduction gear in regard to vehicle energy consumption. Conversely, the configuration with one electric axle and a two-speed transmission exhibits the lowest TCO. Under the China World Transient Vehicle Cycle, the fuel consumption of the diesel truck is reduced by up to 44.59 % with the use of the optimized hybrid configuration. The truck's TCO can be then reduced by up to 832 thousand CNY in one-million-kilometer operation. For an actual road operation, these figures change to 34.79 % and 284 thousand CNY, respectively.

An adaptive assisted method based on MOPSO for THz MMA effective designing

Abstract To address the high time cost and low efficiency of traditional terahertz metamaterial absorber design, an adaptive method based on multi-objective particle swarm optimization was proposed. First, we presented a symmetric absorber of a four-split-ring resonator with a cross-shaped top layer. The structural parameters were optimized by the particle swarm optimization and multi-objective particle swarm optimization algorithms. The simulation results indicated that the multi-objective particle swarm optimization rapidly obtained geometric parameters that balance the high absorptivity and high quality factors. This method quickly identified a symmetric structure with absorptivity greater than 99% and quality factor of 377.45 at 1.584 THz. Additionally, the asymmetric structure achieved near-perfect absorption at 1.585 THz with a Q value of 281.32. By studying the electric field distribution and the surface current distribution, the physical mechanism of the absorber designed has been explained. This adaptive assisted method based on multi-objective particle swarm optimization can efficiently design metamaterial absorbers and offer an artificial intelligence strategy for terahertz functional device design.

Simultaneous Optimization of Exergy and Economy and Environment (3E) for a Multistage Nested LNG Power Generation System

The study introduces an innovative three-stage nested power generation system that enables the cascading utilization of LNG cold energy. It makes the most of wasted energy by using ship jacket cooling water (JCW) and exhaust gas (EG) as heat sources, a trans-critical carbon dioxide cycle as internal circulation, and utilizing the pressure exergy of LNG. We choose two azeotrope mixing fluids that match the requirements and create four cases for the outer and middle cycle working fluids in the three-stage nested system. To discover the ideal system performance from the perspectives of exergy (E), economy (E), and environment (E), four cases were subjected to multi-objective optimization using the multi-objective particle swarm optimization technique (MOPSO). Finally, the optimal solution was found by applying the TOPSIS decision-making method. Through comparative analysis, the optimal system is selected among the four optimization results. R170 (22.66%) and R1150 (77.34%) are used as the outer circulating working medium, while R170 (90.86%) and R1270 (9.14%) are utilized as the inter-cycle working fluid. The net output work is 575.75 kW, the optimal exergy efficiency is 46.09%, the optimal electricity production cost is $0.04009 per kWh, the carbon dioxide emissions can be reduced by 36,910 tons, and the payback period is 2.548 years. After optimization, a more energy-efficient and environmentally friendly power generation system is obtained.

Reinforcement Learning-Based Optimal Hull Form Design with Variations in Fore and Aft Parts

Abstract With recent advancements in artificial intelligence technology, various studies are being conducted in the shipbuilding industry. Traditionally, hull form variation methods have relied on the intuition and expertise of designers, leading to inconsistent results and unintended changes in the ship's main dimensions depending on the designer's competence. Moreover, the iterative process of design variation and analysis to derive the optimal hull form is both costly and time-consuming. To address these issues, this study proposes an optimal hull design technique utilizing reinforcement learning, a type of unsupervised learning in machine learning. Reinforcement learning allows the model to learn from past policies by recording and accumulating the rewards associated with various actions taken by an agent in a specific environment. In this study, after calculating the main parameters of the ship, the agent defines a state representing hull information and performs local transformations of the bow and stern. The reward of reinforcement learning is defined as the change in total resistance due to the hull deformation, constrained by limiting the tolerance of the ship's prismatic coefficient (CP) and longitudinal center of buoyancy (LCB). In this study, the problem is solved by comparing the proximal policy optimization (PPO) algorithm and the deep deterministic policy gradient (DDPG) algorithm to find the best deep reinforcement learning (DRL) model for the hull optimization problem. The results were compared with the genetic algorithm and speed-constrained multi-objective particle swarm optimization (SMPSO), and the optimal hull resistance values were less different, but the time of the reinforcement learning model was five times shorter.

Enhancing energy-efficient building design: a multi-agent-assisted MOEA/D approach for multi-objective optimization

Energy-efficient building design is often challenged by multiple optimization problems due to contradictory objectives that are often hard to balance, so an effective optimization method should be thoroughly considered. Accordingly, a multi-objective evolutionary algorithm is then proposed. Firstly, the multi-agent auxiliary objective evolutionary algorithm for building energy efficiency model is established. According to model result analysis, the proposed algorithm runs fastest for 1640s with the average running time of 1710s in a single-room building, comparing to the least running time of 1680s for the multi-objective particle swarm optimization algorithm. In multi-room buildings, the proposed algorithm runs from 3350s to 3650s, with the average running time of 3500s. In conclusion, the model proposed in this study can comprehensively consider multiple objectives such as energy consumption, cost, comfort, etc. No matter in single-room or multi-room buildings, the model demonstrates superior performance and stability to realize comprehensive optimization of energy conservation design.

Optimization of operational parameters of marine methanol dual-fuel engine based on RSM-MOPSO

Methanol and natural gas, as green fuels for environmental protection, have shown great potential in the field of maritime transportation. This study explores how the methanol energy fraction (MEF), exhaust gas recirculation (EGR), excess air ratio (λ), and engine injection timing (EIT) influence combustion and emission traits in marine dual-fuel (DF) engine. Initially, a DF engine model was formulated through experiments using CONVERGE software, while the CHEMKIN program devised a chemical mechanism involving 100 compounds and 512 reactions. The response surface methodology (RSM) was subsequently utilized to design the experiment and develop the regression model. The improved multi-objective particle swarm optimization (MOPSO) algorithm was applied to optimize three critical variables: brake thermal efficiency (BTE), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. Ultimately, feature-optimized scenarios from the Pareto front were chosen for comparative evaluation to derive the optimal configuration. The results show that when the decision variables MEF, EGR, λ and EIT are 22.33 %, 10 %, 1.81 and 11°CA BTDC respectively, there is a better trade-off between the three target variables. Compared with the original case, the change of ITE, NOx and CO emissions in the optimal case are +0.97 %, −39.53 % and −14.51 %, respectively. Overall, based on the intelligent optimization approach, the rational selection of DF engine parameters improved the ITE and most NOx and CO emissions were effectively suppressed.

Multi-objective optimization of chemical reaction characteristics of selective catalytic reduction in denitrification of diesel engine using ELM-MOPSO methodology

This study systematically investigated the effects of structural parameters in a two-stage selective catalytic reduction (SCR) catalyst on ammonia storage, NO conversion efficiency, and ammonia slip using computational fluid dynamics (CFD) simulations. The rank ordering of the correlations between structural parameters and performance metrics was determined by gray relational analysis (GRA). Both the catalyst diameter and the upstream catalyst porosity had correlations exceeding 0.9, making them key parameters affecting SCR performance. An extreme learning machine (ELM) prediction model was trained using data obtained from CFD simulations. The prediction performance of this ELM model was then evaluated using statistical metrics. Finally, the optimized Pareto frontier diagram was obtained through the multi-objective particle swarm optimization (MOPSO) algorithm, and the optimal results were selected for further CFD modeling analysis. The results showed that the upstream NO conversion efficiency of the SCR system was improved by 7.7 %, the downstream NO conversion efficiency was improved by 18.67 %, the ammonia slip was reduced by 71.97 %, and the upstream ammonia storage capacity was improved by 6.83 % compared with the original model.

Structural Performance Analysis and Optimization of Small Diesel Engine Exhaust Muffler

In recent years, the optimization of diesel engine exhaust mufflers has predominantly targeted acoustic performance, while the impact on engine power performance has often been overlooked. Therefore, this paper proposes a parallel perforated tube expansion muffler and conducts a numerical analysis of its acoustic and aerodynamic performance using the finite element method. Then, a Kriging model is established based on the Design of Experiments to reveal the impact of different parameter couplings on muffler performance. With transmission loss (TL) and pressure loss (PL) as the optimization objectives, a multi-objective optimization study is carried out using the competitive multi-objective particle swarm optimization (CMOPSO). The optimization results indicate that this method can simplify the optimization model and improve optimization efficiency. After CMOPSO calculation, the average TL of the muffler increased from 27.3 dB to 31.6 dB, and the PL decreased from 1087 Pa to 953 Pa, which reduced the exhaust noise and improved the fuel economy of the engine, thus enhancing the overall performance of the muffler. This work provides a reference and guidance for the optimal design of mufflers for small agricultural diesel engines.

Multi-objective optimization of helical gear transmission geometric parameters using MOPSO

Abstract Helical gears are widely used in mechanical devices, but insufficient tooth strength can easily lead to premature failure, which is an urgent problem to be solved. To overcome this challenge, this paper introduces a multi-objective particle swarm optimization algorithm, which comprehensively considers the comprehensive effects of modification coefficient, number of teeth, and modulus on the performance of helical gears. By constructing a mathematical model with the geometric parameters of helical gears as the core design variables, aiming to minimize the difference between the maximum bending stress of helical gear roots and the contact stress on the tooth surface, we have achieved optimization of gear performance. Using MATLAB for algorithm programming, the established model was efficiently optimized and solved. After optimization, the bending strength difference and tooth surface bearing capacity of the gear pair have been significantly improved, ensuring the stability and reliability of the operation of the high-pressure sleeve disassembly transmission device.

Adaptive hierarchical energy management strategy for fuel cell mobile robot hybrid power system based on working condition recognition

Energy management of hybrid power is critical to maintain the economical and efficient operation of fuel cell mobile robots. To improve the energy distribution between the proton exchange membrane fuel cell (PEMFC) and battery under different working conditions, this paper proposes an adaptive hierarchical energy management strategy (AHEMS) based on the recognition and management levels. Firstly, the recognition level realizes the identification of different working conditions based on the machine learning (ML) methods including the K-means and KNN. Secondly, the fuel cell hydrogen consumption and efficiency are both optimized by adaptive multi-objective particle swarm optimization (AMOPSO) at the management level. Specifically, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Besides, to overcome the parameter uncertainty caused by different working states and improve the system performance, an interval optimization scheme is proposed based on the Pareto solutions. Finally, the fuzzy decision combined with the recognition results and state of charge (SOC) of the battery is performed to find the most appropriate power distribution of the PEMFC and battery. The proposed AHEMS algorithm is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. These results demonstrate that the hybrid power system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the hydrogen consumption and efficiency indexes, revealing the success of this AHEMS approach in solving the energy distribution problem in different working conditions.