Multi-objective Optimization Algorithm Research Articles

Structural optimization of serpentine channel water-cooled plate for lithium-ion battery modules based on multi-objective Bayesian optimization algorithm

Maintaining the battery within its optimal operating temperature range and preventing thermal runaway is crucial. Serpentine channel water-cooled plate (SCWCP) has been widely employed in battery pack cooling. The challenge lies in enhancing the cooling efficiency of SCWCP while minimizing energy consumption. Due to the high efficiency and robustness of the multi-objective Bayesian optimization (MOBO), it is employed to systematically optimize the SCWCP for lithium batteries. The width, depth, and turning radius of the serpentine flow channels are optimized to minimize both the maximum battery module temperature (Tmax) and the pumping power (PP) of the SCWCP. The MOBO process integrates structural parameter adjustments into Computational Fluid Dynamics (CFD) simulations through an automated iterative approach. Subsequently, a Pareto front is generated based on Tmax and PP, and the K-means clustering algorithm identifies four design solutions with different performance orientations. Compared with the initial design, Tmax of the optimal design decreases slightly, but with a reduction in PP of 71 %. Compared to other evolutionary algorithms, MOBO algorithm exhibits superior computational efficiency in providing an optimal design solution set at a lower computational cost.

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

Parametric optimization of bio-inspired engineered sandwich core

The present study aims to design an efficient honeycomb cell structure for enhanced energy absorption. Elytra and bamboo bio-inspired parts were compared using a multi-criteria decision-making methodology (COPRAS) and finite element analysis (through Abaqus/CAE) to select the optimal candidate geometry for the study. A circular elytra-inspired geometry featuring four reinforcing cylinders was selected, demonstrating an increase in Specific Energy Absorption (SEA) of over 68% compared to a baseline geometry of the same mass. Structure optimization, aided by a genetic algorithm (NSGA-II), significantly improved crashworthiness parameters, presenting optimized values for design variables, This resulted in an increase in SEA by up to 94% and a 34% improvement in Crushing Force Efficiency (CFE) compared to a baseline geometry. The robust correlation between the algorithm and Finite Element Method (FEM) results highlights its usefulness for initial design, reducing computational demands. The research selects a circular elytra-inspired geometry featuring four reinforcing cylinders and showcasing the potential of multi-objective optimization algorithm in conjunction with FEM analysis in creating high-performance, lightweight structures for passive safety in aeronautics.

Multi-objective algorithm for hybrid microgrid energy management based on multi-agent system

In the dynamic landscape of renewable energies, microgrid systems emerge as a promising avenue for fostering sustainable local energy generation. However, the effective management of energy resources holds the key to unlocking their full potential. This study assumes the task of creating a multi-objective optimization algorithm for microgrid energy management. At its core, the algorithm places a premium on seamlessly integrating renewable energy sources and orchestrating efficient storage coordination. Leveraging the prowess of a multi-agent system, it allocates and utilizes energy resources. Through the combination of renewable sources, storage mechanisms, and variable loads, the algorithm promotes energy efficiency and ensures a steady power supply. This transformative solution is underscored by the algorithm's remarkable performance in practical simulations and validations across diverse microgrid scenarios, offering a prevue into the future of sustainable energy utilization.

A novel penalty function-based interval constrained multi-objective optimization algorithm for uncertain problems

To directly address problems involving uncertain objectives and constraints, a novel penalty function-based interval constrained multi-objective optimization algorithm (PF-ICMOA) was developed. It comprises three main components: an uncertainty propagation-based interval constraint solver, an interval constraint violation function, and a two-stage penalty function. The uncertainty violation of interval constraints can be accurately evaluated by the proposed advanced multi-objective optimization algorithm using a segmentation function without requiring multiple interval analyses. Additionally, by applying penalties to infeasible solutions in the two-stage process, partially feasible and superior infeasible solutions are retained in the early stages of evolution, while superior feasible solutions are favored in the later stages. A novel benchmark was designed and the effect of the imprecision factor on the Pareto front was examined in details. Comparisons with seven deterministic constraint handling techniques demonstrated the effectiveness of the proposed method for solving interval constrained multi-objective optimization problems, demonstrating that PF-ICMOA is a highly competitive method for balancing the feasibility, convergence, and diversity performance characteristics.

Improved dragonfly optimization algorithm based on quantum behavior for multi-objective optimization of ethylene cracking furnace

Ethylene is one of the important basic raw materials for the modern chemical production, accounting for over 75 % of petrochemical products. And the ethylene cracking furnace is subject to coking problems that can impede the thermal conductivity of the reaction tube. Therefore, an improved multi-objective dragonfly optimization algorithm based on quantum behavior is proposed. The particle evolutionary model introduces the quantum bound state δ-potential well model, which improves the global convergence ability and the convergence speed of the dragonfly optimization algorithm. In addition, the calculation formulas for parameters are improved by introducing natural logarithm and the iterative search method is changed to a triple search. Moreover, the global convergence and local search ability of the quantum behavior enhancement algorithm is assisted by adaptively selecting the search strategy according to the current number of iterations of the improved dragonfly algorithm. Compared with other multi-objective optimization algorithms on the ZDT and CEC test functions, the proposed algorithm has good global convergence and local search capabilities in terms of the inverted general distance. Finally, the proposed algorithm is applied in the multi-objective optimization of an ethylene cracking furnace, with the ethylene yield and the propylene yield as optimization objectives. The experimental results show that the ethylene yield can be increased by 1.5498 %, while the propylene yield decreases by only 0.0081 %, which proves that the proposed algorithm achieves better effect in optimizing the main product yield under fixed cycle adjustment operating conditions compared to original operating conditions.

Multi-Physics Coupled Acoustic-Mechanics Analysis and Synergetic Optimization for a Twin-Fluid Atomization Nozzle

Fine particulate matter produced during the rapid industrialization over the past decades can cause significant harm to human health. Twin-fluid atomization technology is an effective means of controlling fine particulate matter pollution. In this paper, the influences of the main parameters on the droplet size, effective atomization range and sound pressure level (SPL) of a twin-fluid nozzle (TFN) are investigated, and in order to improve the atomization performance, a multi-objective synergetic optimization algorithm is presented. A multi-physics coupled acoustic-mechanics model based on the discrete phase model (DPM), large eddy simulation (LES) model, and Ffowcs Williams-Hawkings (FW-H) model is established, and the numerical simulation results of the multi-physics coupled acoustic-mechanics method are verified via experimental comparison. Based on the analysis of the multi-physics coupled acoustic-mechanics numerical simulation results, the effects of the water flow on the characteristics of the atomization flow distribution were obtained. A multi-physics coupled acoustic-mechanics numerical simulation result was employed to establish an orthogonal test database, and a multi-objective synergetic optimization algorithm was adopted to optimize the key parameters of the TFN. The optimal parameters are as follows: A gas flow of 0.94 m3/h, water flow of 0.0237 m3/h, orifice diameter of the self-excited vibrating cavity (SVC) of 1.19 mm, SVC orifice depth of 0.53 mm, distance between SVC and the outlet of nozzle of 5.11 mm, and a nozzle outlet diameter of 3.15 mm. The droplet particle size in the atomization flow field was significantly reduced, the spray distance improved by 71.56%, and the SPL data at each corresponding measurement point decreased by an average of 38.96%. The conclusions of this study offer a references for future TFN research.

Open-source multi-objective optimization software for menu planning

Healthy nutrition is essential for maintaining health and preventing chronic diseases. However, with the increase in dining out, the prevalence of obesity and related chronic diseases is increasing daily. Therefore, to provide adequate and balanced nutrition in public, it is necessary to serve healthy menus following the energy and nutrient requirements of the target group in food service institutions. However, menu planning in food services is a complex process involving several factors. The planned menus should provide sufficient nutrients for the target group and consider factors such as color, consistency, appearance, and a variety of food groups. In addition, existing studies have limited constraints, and the fact that they are not open-source makes it difficult to conduct more comprehensive new studies. Furthermore, the lack of dietitian opinions limits its applicability in practice. Therefore, this study applied four different multi-objective optimization algorithms (AGEMOEA, SMSEMOA, NSGA2, and NSGA3) to solve the menu planning problems. The results were shared with 20 food service dietitians who are experts in the area and were asked to score the menus. In addition, an open-source tool called EvoMeal has been developed. In conclusion, our primary findings showed that AGEMOEA and SMSEMOA were used for the first time in the literature for the menu planning problem, which is a multi-objective problem, and they performed better than both NSGA2 and NSGA3 in general. In addition, expert opinions confirmed that AGEMOEA and SMSEMOA give better results than other algorithms. However, future studies need to conduct a more comprehensive expert evaluation to increase its applicability to the field.

Quantum-inspired multi-objective African vultures optimization algorithm with hierarchical structure for software requirement

Quantum-inspired multi-objective African vultures optimization algorithm with hierarchical structure for software requirement

Heuristic algorithms for design of integrated monitoring of geologic carbon storage sites

Designs for Risk Evaluation and Management (DREAM) is a tool developed under the National Risk Assessment Partnership (NRAP) to enhance geologic carbon storage safety and efficiency. Using potential leakage scenarios generated externally by the users preferred history-matching approach, DREAM constructs ideal combinations of sensor locations in the right place at the right time to detect as many leaks as possible, detect them as early as possible, and minimize cost. This user-friendly tool, developed in Java, features a window-based GUI for input and a 3D visualization tool for viewing the domain space and optimized monitoring plans. DREAM's latest version accommodates real-world usage by allowing for joint optimization of wellbore point sensor placements and surface geophysics survey geometries, and by using more efficient multi-objective optimization algorithms. In an example shown here, these two improvements combined allow us to support containment assurance and go from detecting 80–90 % of the potential CO2 leakage to +99.7 %, a step-change improvement that can make the deciding difference in whether a site is suitable for geologic carbon storage. Though developed for geologic carbon storage, this tool would be equally applicable in many surface or offshore environmental monitoring projects.

Phased optimization of active distribution networks incorporating distributed photovoltaic storage system: A multi-objective coati optimization algorithm

In this study, a phased operation optimization method for active distribution network with energy storage system is proposed for the operation optimization problem of active distribution network. The proposed model considers the PV and storage system output, the number of regulation equipment action, network loss and node voltage deviation for multi-objective optimization of the active distribution network. To achieve multi-objective optimization of the proposed optimal model, this study proposes a multi-objective coati optimization algorithm based on integrated crowding distance ranking, Bernoulli chaotic mapping, external archiving and non-dominated ranking mechanism. The proposed algorithm exhibits good performance as tested by ZDT and UF test functions, and obtaining optimal values in 75 % of the results. In the test of the improved IEEE30 and IEEE33 node system, the proposed method is improved by 21.55 %, 56.10 % and 4.12 % in the three aspects of network loss, node voltage deviation and minimum voltage of the whole network compared with the unoptimized case. Therefore, the proposed method in this study has a good application value for the optimization of active distribution network operation and can support the grid-connected operation of distributed new energy sources.

Design of an optically transparent and broadband absorber based on a multi-objective optimization algorithm

In this Letter, an optically transparent and broadband absorber designed using a multi-objective genetic algorithm (MOGA) is proposed. The absorption of the multilayer lossy frequency selective surface-based absorber is calculated by multilayer absorption equations and equivalent circuit models. To solve the problem of the unbalanced structure absorption bandwidth and thickness, an algorithm is used for optimizing the geometric and sheet resistance parameters of the structure. A multilayer and optically transparent absorber with 90% absorption bandwidth covering a frequency range of 2–18 GHz (S-band to Ku-band) is developed based on the MOGA design method with optical transmittance of 60%. Its total thickness consists of a wavelength of only 0.095, and it has high oblique incidence stability, which makes it useful in the stealth technology and transparent electromagnetic shielding applications.

Optimization of dual-function suspension structures using particle swarm optimization approaches

The suspension system integrating both vibration control and energy harvesting capabilities is denoted as Dual-function Suspension (DFS). The principal objectives for DFS encompass lightweight structure, high output force, extensive adjustability in damping, and minimized energy consumption. In pursuit of optimizing the linear motor and magnetorheological damper (MRD) amalgamated into the DFS, a multi-objective Particle Swarm Optimization (PSO) algorithm is conceived, emphasizing primary and secondary objectives to enhance the holistic performance of the DFS. A comprehensive mathematical model of the DFS is established, and subsequent to this modeling, the structural parameters of DFS are meticulously analyzed. Drawing upon the insights from this analysis, primary and supplementary optimization objectives are delineated, employing PSO for the refinement of the DFS structure. Following this, the Pareto solution set, derived from the optimization process, is judiciously selected utilizing fuzzy theorem principles. The outcomes reveal that, under the constraints of unaltered suspension packaging dimensions and overall energy consumption, the optimized suspension system manifests a 50% augmentation in output force, a 30% expansion in adjustable damping range, and a 39% reduction in thrust ripple compared to its pre-optimized state.

Gradient-based algorithms for multi-objective bi-level optimization

Gradient-based algorithms for multi-objective bi-level optimization

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

ETE-SRSP: An Enhanced Optimization of Tramp Ship Routing and Scheduling

In the contemporary tramp shipping industry, route optimization and scheduling are directly linked to enhancements in operations, economics, and the environment, making them key factors for the effective management of maritime transportation. To enhance effective ship-to-cargo matching and the refinement of maritime transportation itineraries, this paper introduces a time efficiency and carbon dioxide emission multi-objective optimization algorithm named ETE-SRSP (efficiency–time–emission multi-optimization algorithm). ETE-SRSP incorporates several factors, including the initial positions of ships, time windows for loading and unloading operations, and varying sailing speeds. Within the ETE-SRSP framework, pioneering an approach that integrates ballast and laden sailing velocities as decisional parameters, it employs a multi-objective optimization technique to investigate the intricate interplay between temporal efficiency and carbon dioxide emissions. Additionally, the model’s proficiency in mitigating emissions and managing costs is clearly demonstrated through the optimization of these objectives, thereby offering a robust framework for decision support. The experimental results show that the optimal sailing speeds derived from the ETE-SRSP, under typical time-weight scenarios, can achieve an optimal balance between emission reduction and cost control. In summary, this study underscores the optimization strategy’s potential to effectively address the maritime sector’s need for economic growth and ecological conservation, showcasing its practical value in the industry.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Reactive Power Optimization in Distribution Networks of New Power Systems Based on Multi-Objective Particle Swarm Optimization

The new power system effectively integrates a large number of distributed renewable energy sources, such as solar photovoltaic, wind energy, small hydropower, and biomass energy. This significantly reduces the reliance on fossil fuels and enhances the sustainability and environmental friendliness of energy supply. Compared to distribution networks in traditional power systems, the new-generation distribution network offers notable advantages in improving energy efficiency, reliability, environmental protection, and system flexibility, but it also faces a series of new challenges. These challenges include potential harmonic issues introduced by the widespread use of power electronic devices (such as inverters for renewable energy generation systems and electric vehicle charging stations) and the voltage fluctuations and flickering caused by the intermittency and uncertainty of renewable energy generation, which may affect the normal operation of electrical equipment. To address these challenges, this study proposes an optimization model aimed at minimizing network losses and voltage deviations, utilizing traditional capacitor adjustments and static var compensators (SVCs) as optimization measures. Furthermore, this study introduces an improved version of the multi-objective particle swarm optimization (MOPSO) algorithm, specifically enhanced to address the unique challenges of reactive power optimization in modern distribution networks. The test results show that this algorithm can effectively generate a large number of Pareto optimal solutions. The application of this algorithm on a 33-node network case study demonstrates its advantages in reactive power optimization. The optimization results highlight the effectiveness and feasibility of the proposed improved algorithm in the application of distribution network reactive power optimization, offering users a uniform and diverse range of reactive compensation solutions.

A Multi-Objective Pigeon-Inspired Optimization Algorithm for Community Detection in Complex Networks

Community structure is a very interesting attribute and feature in complex networks, which has attracted scholars’ attention and research on community detection. Many single-objective optimization algorithms have been migrated and modified to serve community detection problems. Due to the limitation of resolution, the final algorithm implementation effect is not ideal. In this paper, a multi-objective community detection method based on a pigeon-inspired optimization algorithm, MOPIO-Net, is proposed. Firstly, the PIO algorithm is discretized in terms of the solution space representation, position, and velocity-updating strategies to adapt to discrete community detection scenarios. Secondly, by minimizing the two objective functions of community score and community fitness at the same time, the community structure with a tight interior and sparse exterior is obtained. Finally, for the misclassification caused by boundary nodes, a mutation strategy is added to improve the accuracy of the final community recognition. Experiments on synthetic and real networks verify that the proposed algorithm is more accurate in community recognition compared to 11 benchmark algorithms, confirming the effectiveness of the proposed method.