Earticle

이 논문은 다목적 최적화 알고리듬을 이용하여 오일샌드 저류층에서 생산성이 높으면서도 최적의 에너지효 율을 갖는 SAGD(Steam Assisted Gravity Drainage) 공법의 운영조건을 선정하는 기법을 제안한다. 기존의 SAGD 공 법 최적화 연구들은 경제성인자가 고정된 단목적 최적화에 집중하였다. 이 연구에서는 회수율 최대화와 누적증기오 일비 최소화 관점에서 비지배관계에 놓이는 최적 생산운영 시나리오를 선정하였다. 이를 통하여 특정 목적함수에 편 향된 하나의 최종해만을 도출하는 기존 방법의 한계를 개선하였다. 제안한 방법으로 선정된 최적운영 시나리오들은 기존 방법과 달리 유가상황 및 비용 변화에 따라 추가적인 최적화 작업 없이 광구운영 의사결정에 유용하게 활용할 수 있다.

This paper proposes multi-objective optimization of the Steam Assisted Gravity Drainage (SAGD) process for improving the energy efficiency and recovery factor of oil sand reservoirs. Previous studies conducted on optimizing the SAGD process have focused on single-objective optimization with fixed economic factors. In this study, multiple trade-off operating scenarios were selected by applying a multi-objective optimization algorithm that aims at maximizing the recovery factor and minimizing the cumulative steam-oil ratio for efficiently addressing volatile market conditions. Thus, the proposed method can overcome the limitations of conventional optimization methods that not only yield a single solution based on a particular objective function but also are hard to adapt to fluctuating oil prices. Furthermore, the proposed method can provide optimum trade-off operating scenarios, and hence can aid in planning operating (i.e., marketing) strategies according to the variation in oil prices and operating costs, without the need for an additional optimization process.

According to Genetic algorithms principle, the new hybrid evolutionary algorithm (HEA) is proposed in this paper by combining the Immune algorithm, Genetic algorithm and Pareto optimal solutions. The HEA has high convergence precision and improved the diversity of population. Multiple near optimization paths can be developed by the algorithm with multi‐objective restriction, and satisfy to minimize the routing of transportation and the numbers of the vehicles. The HEA has been used to solve the vehicle routing problem, the results of simulation experiment show that the HEA can gain higher global convergence rate and higher speed.

In various machines used in industrial sites and transportation equipment, fastening structures of bolts and nuts are widely employed. However, conventional Steel sockets, classified as non-explosion-proof materials, have a high likelihood of generating sparks due to friction with components, which can lead to explosions or large-scale fires. To address this issue, this study developed a lightweight explosion-protection socket using AL-7075-T6 aluminum alloy, which is known for its excellent explosion-proof properties. However, due to the inherent characteristics of aluminum, it has lower rigidity compared to Steel, requiring the use of more expensive alloy materials. Therefore, our research team utilized Finite Element Analysis (FEA) and Multi-Objective Genetic Algorithm (MOGA) to optimize the mass and safety factor of the socket, proposing a design that simultaneously achieves both weight reduction and structural stability. The socket developed in this study is approximately 30% lighter than traditional Steel-based sockets while maintaining a safety factor of 1.2 or higher, significantly enhancing operational safety in explosive environments. This research sets a new standard in the design and manufacturing process of explosion-proof sockets and is expected to contribute to the optimization of various explosion-proof equipment in the future.

For the large scale services with high-dimensional QoS attributes and distributed environment, traditional service selection approaches are faced with unprecedented challenges in terms of efficiency and performance of QoS. To address these challenges, we propose a three-phase large scale Skyline service selection framework for service composition in clouds. This framework adopts distributed parallel Skyline computation with MapReduce to prune redundant candidate services, and employs parallel multi-objective optimization algorithm based on MapReduce to select Skyline services from the tremendous amount of Skyline services warehouse for composing single service into a set of more powerful Skyline composite services, then applies Top-k query processing technology or multiple attribute decision making support method to select k Skyline composite services from the set of Skyline composite services. Through theoretical analysis, the framework can efficiently solve the service selection problem with large scale services, high-dimensional QoS in cloud computing environment, and quickly generate better composite services with the global optimal QoS.

Biogeography-based optimization (BBO) has shown excellent exploitation ability of the population information for simple-objective optimization problem. But if BBO is directly applied in multi-objective optimization problems (MOPs), optimal solution set gained by BBO has worse diversity and distribution. To overcome these shortcomings, a chaos migration operator is put forwards to improve the diversity of the population. And then based on the new chaos migration operator, Chaos biogeography multi-objective optimization algorithm (CBBMO) is proposed for MOPs. In CBBMO, the chaos migration operator and original mutation operator of BBO are applied to produce the next generation population. The archive is used to conserve the Pareto optimal solutions. The experiment results show that the proposed algorithm CBBMO is feasible and effective for MOPs.

As a commercial distributed computing mode, cloud computing needs to meet the quality of service (QoS) requirement of users, which is its top priority. However, cloud computing service providers also need to consider how to reduce the overhead of data center, and keep load balancing is one of the key points to maximize the use of the resource in the data center. In this paper, we propose an improved multi-objective niched Pareto genetic algorithm (NPGA) to take load balancing into consideration without affecting performance of time consumption and financial cost of handling the user’s cloud computing tasks by presenting the load balancing shift mutation operator. The simulation results and analysis show that the proposed algorithm performs better than NPGA in maintaining the diversity and the distribution of the Pareto-optimal solutions in the cloud tasks scheduling under the same population size and evolution generation.

In this paper, based on Magnetotactic Bacteria Optimization Algorithm(MBOA), magnetotactic bacterium multi-objective optimization algorithm (MBMOA) is proposed for solving multi-objective optimization problems(MOPs). Magnetotactic bacterium optimization algorithm is a novel random research algorithm which simulates the process of magnetotactic bacteria (MTB) producing magnetosomes(MTS) to regulate cell moment and makes the magnetostatic energy reach the minimum .The algorithm MBOA proposed three operators named by MTS producing, MTS amplification and MTS replacement by imitating the development process of magnetosomes, the adjustment process of magnetosomes moment and the replacement process of magnetosome with worse moment. In MBMOA, MBOA is applied to produce the next population, while non-dominated feasible solutions gained by MBOA are conserved in the archive, then the evaluation method of SPEA2 is adopted to update the archive, at the last through benchmark functions test and classic algorithm comparison, the simulation results show that the MBMOA is feasible and effective for solving multi-objective optimization problems.

Due to the cost, reliability and quality of communication equilibrium of the traditional FM radio network optimization algorithms during the construction , hierarchical multi-objective optimization algorithm is put forth.First, increasee FM wireless networks mobile node and select new-adding link waypoints’ optimal vector set, then design a hierarchical optimization model, and then use hierarchical multi-objective optimization algorithm to solve the problems of VHF wireless networks.Finally, compared simulation experiment of stratified multi-objective optimization algorithm and Glid algorithm and violent search algorithm is conducted, and the experiment shows that: stratified multi-objective optimization algorithm is smaller than traditional optimization algorithms in areas such in the network nodes, the average communication jump and the average attenuation of communication after running 50 times, i.e. lower cost, better network reliability and communication quality.

In this paper, chaos theory and the traditional multi-objective optimization evolutionary algorithm is put forward, "Chaos-based multi-objective evolutionary algorithm", combines a variety of optimization strategies. The traditional multi-objective evolutionary algorithm for repeating individual causes of variation is based on chaotic analysis of multi-objective evolutionary algorithm and demonstration. According to the characteristics of chaotic map tent, NSGA-II algorithm in this paper on the basis of chaotic map was proposed based on chaotic tent initialization and chaotic mutation multi-objective evolutionary algorithm. The original NSGA-II algorithm is improved, and the introduction of adaptive mutation operator and a new crowding distance is calculated and applied to the design of the algorithm. Analysis and experimental results show that these methods can better improve the distribution of population performance.

Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm. The algorithm uses a data warehouse to store the optimal solution produced by individuals in each generation, from the way individuals adopt measures to phase out the individual data warehouse identical or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding between sets, can get a lot of optimal and balanced.

A new hybrid Optimization Algorithm is proposed to solve Constrained Multi-objective Optimization Problems (CMOPs). The algorithm is named BBO/DE which combines the exploitation ability of Biogeography-based Optimization (BBO) and the exploration ability of Differential Evolution (DE). Meanwhile distance measures and adaptive penalty functions are adopted to handle the constraints so that optimal solutions in the infeasible space can be searched effectively. In addition, the feasible archive is applied to store the non-dominated feasible solutions obtained so far and is updated based on crowding-distance. Experiment results demonstrate that the proposed hybrid algorithm BBO/DE can approximate the true Pareto front and has better distribution.

This paper presents an operating and cost optimization model for Micro Grid (MG). This model takes into account emission costs of CO2, SO2 and NOx, together with the operation, maintenance and startup costs. Photovoltaic (PV) arrays, Wind Turbine (WT), Fuel Cell (FC), Micro Turbine (MT) and Diesel Generator (DG) with different capacities are considered in this model. The aim of the optimization is minimizing operation cost according to constraints, supply demand and safety of system. The proposed Bender’s Decomposition (BD) is used to optimize the micro grid operation.

To effectively optimize multi-objective logistics distribution path, the distance and distance related customer satisfaction factor are used as the objective function, a novel kruskal crossover genetic algorithm (KCGA) for multi-objective logistics distribution path optimization is proposed. To test the optimization results, the terminal distribution model and the virtual logistics system operating model are built. Experiment results show that, compared with basic genetic algorithm (GA), the run time of KCGA takes a slightly higher. But the average distribution distance and the best distribution distance are reduced by 6%-8%. Achieve the goal of multi-objective logistics distribution path optimization.

This study presents a robust thermoeconomic framework for marine nuclear propulsion, utilizing the Nuclear Ship Savannah as a case study. To overcome data limitations, creative solutions are applied, including the application of artificial neural network approach to solve nonlinear coupled exergoeconomic equations and determine the thermodynamic properties of the flow within the Savannah's Nuclear Propulsion (SNP). Upon validating the proposed model, the study introduces innovative design modifications to enhance the thermal efficiencies of the SNP. Furthermore, single-objective optimization (SOO) and multi-objective optimization (MOO) are incorporated into the improved SNP (ISNP) to optimize system performance criteria, such as exergy efficiency (η_II), total capital cost rate (Ċ^I&O&M_tot), and total product exergy cost rate (Ċ_P). Afterwards, the ISNP optimization is executed using Henry Gas Solubility Optimization (HGSO) to determine the optimal non-dominated solutions, allowing for the evaluation of trade-offs in system performance. The final optimal design of the optimized ISNP (OISNP) is then selected through a multi-criteria decision-making process. Subsequently, a comprehensive comparative analysis is conducted on the SNP, ISNP, and OISNP models, considering energy, exergy, exergoeconomic, and environmental economics (4-E) viewpoints. The 4-E analysis reveals that the SNP exhibits an energy efficiency of 26.18 % and an exergy efficiency of 51.8 %, whereas the ISNP demonstrates enhanced performance with an energy efficiency of 28.42 % and an exergy efficiency of 55.50 %. The improvement lead to a 1.31 MW boost in propulsion power compared to the SNP, bringing it to 16.4 MW. This comparative evaluation underscores the significant improvements achieved through the modifications implemented in the SNP. Also, the OISNP demonstrates further advancements, achieving an exergy efficiency of 55.01 %, representing a 3.2 % improvement over the SNP, while its energy efficiency is recorded at 28.4 %, a 2.22 % increase compared to the SNP. Additionally, the propulsion power reaches 16 MW, marking a 1 MW increase over the SNP. Notably, the Ċ^I&O&M_tot,OISNP and Ċ_P,OISNP are reduced to $282.3/hour and $4290/hour, respectively, indicating substantial cost savings relative to both the ISNP and SNP models. Besides, the environmental analysis indicates that transitioning from a fossil fuel-powered MSP to an OISNP annually cuts fossil fuel consumption by 61,320 tons, generating $30.66 million in potential export revenue. This change reduces emissions by 193 kilotons (kt) of CO₂, 4.3 kt of NO_x, 3 kt of SO₂, and 0.92 kt of PM, while decreasing external costs by $6.132 million and potentially avoiding $9.65 million in annual carbon tax liabilities for the shipping industry. Furthermore, to evaluate the comparative statistical performance of the competing algorithms, a Wilcoxon rank sum test is conducted. The comparative results show that the HGSO algorithm outperforms the others, delivering both competitive and superior outcomes in solving complex optimization problems.

The NVH (noise, vibration, harshness) performance of a motor is one of the main problems affecting the comfort, safety, and reliability of electric vehicles. Electromagnetic force is the main cause of motor noise. Most of the existing research focuses on the overall noise level, and does not consider the impact of specific orders of electromagnetic force on noise, which results in a lack of applicability of noise reduction techniques. In this paper, a rotor with an auxiliary slot was used to weaken the electromagnetic force. A multi-objective optimization algorithm combining finite-element simulation with a response surface method was proposed. To determine the relationship between specific orders of electromagnetic force and the auxiliary slot parameters, simulation experiments were carried out with a large range and a large step size in finite-element analysis software. Then, the parameter range with a low value of electromagnetic force was selected. In this new range, the response surface method was used to establish the parameter and electromagnetic force expressions. Then, the linear weighting method in the multi-objective optimization algorithm was selected to determine the objective function of the multi-order electromagnetic force optimization. The weight of each order of electromagnetic force was set according to its contribution to the noise. Finally, the effectiveness of the proposed method was verified by simulations. Simulation results show that this method can quickly and effectively determine the optimal size of the auxiliary slot. In addition, the maximum value of the noise was reduced from 107.6 to 103.2 dB.

Laminate composite structures are employed in industries due to their exceptional stiffness-to-weight ratio and directional properties. Literature has investigated the pursuit of optimal designs for such structures. In this paper, water cycle algorithm (WCA) is utilized as a metaheuristic optimizer algorithm for single and multi-objective optimization problems. Design variables include the number of layers and the orientation angle of fibers within each layer. A comparative analysis is performed to evaluate the efficacy of the WCA in comparison to other well-established optimizers. The obtained optimization results yield valuable insights into laminated composite structures, affirming the applicability of the proposed methodology in composite structural design.

Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

$\textbullet$ Co-evolutionary algorithms $\textbullet$ Nash Genetic Algorithms $\textbullet$ Multi-objective Optimization $\textbullet$ Distance dependent mutation $\textbullet$ Pareto Optimality

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.