Hybrid Multi-objective Optimization Research Articles

Design of Steam Alternating Solvent Process Operational Parameters Considering Shale Heterogeneity

Summary The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs. A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.

A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method’s performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power.

A Hybrid Multi-Objective Particle Swarm Optimization with Central Control Strategy

In recent years, researchers have solved the multi-objective optimization problem by making various improvements to the multi-objective particle swarm optimization algorithm. However, we propose a hybrid multi-objective particle swarm optimization (CCHMOPSO) with a central control strategy. In this algorithm, a disturbance strategy based on boundary fluctuations is first used for the updated new particles and nondominant particles. To prevent the population from falling into a local extremum, some particles are disturbed. Then, when the external archive capacity reaches the extreme value, we use a central control strategy to update the external archive, so that the archive solution gets a good distribution. When the dominance of the current particle and the individual best particle cannot be determined, to enhance the diversity of the population, the combination method of the current particle and the individual best particle can be used to update the individual best particle. The experimental results show that CCHMOPSO is better than four multi-objective particle swarm optimization algorithms and four multi-objective evolutionary algorithms. It is a feasible method for solving multi-objective optimization problems.

A Multi-objective Hybrid Optimization for renewable energy integrated Electrical Power Transmission Expansion Planning

Due to the large size of conventional electrical power transmission systems and the large number of uncertainties involved, achieving the most favourable Transmission Expansion Planning solution turns out to be almost impossible. The proposed method intends to develop a novel method to solve Transmission Expansion Planning problems in electric power systems incorporating renewable energy sources like wind turbines and Photo Voltaic array using IEEE 24 Reliability Test System. For enhancing the efficiency of search processes and to make its use easier on diverse networks and operations, the hybridization of two renowned meta-heuristic algorithms known as Grey Wolf Optimization (GWO) and Genetic Algorithm (GA) termed as Grey Wolf with Genetic Algorithm (GWGA) is adopted. A novel distance factor based on the best position and current position of the solution in Grey wolf optimization is introduced and proposed for the hybridization technique and gives a quick and promising solution with reduced computational time. The GWO and GA algorithms are combined suitably to achieve the advantages of both algorithms. With this proposed model, the investment cost of the transmission line and the maximum amount of power that can be distributed to the consumer is optimized with an objective of minimum load shedding. Among the state-of-the-art optimization techniques considered, a remarkable performance percentage improvement in the expansion plan in terms of cost reduction and load shedding minimization has been obtained in GWO, but when hybridized with GA, an improvement of 13.42% in cost function and 18.65% in load shedding is achieved for a population size of 60. Hence, the proposed method guarantees to generate the best solution with a faster convergence resulting in reduced computational time.

Comparative analysis of DoE-based hybrid multiobjective optimization techniques for inclined laser drilling of glass fiber reinforced plastics

Comparative analysis of DoE-based hybrid multiobjective optimization techniques for inclined laser drilling of glass fiber reinforced plastics

Hybrid multi-objective optimization approach intended for mobile robot path planning model

In the field of mobile robot decision making and control, path planning is an essential element as it defines the performance of the design. It is one of the hot topics in artificial intelligence and researchers pay more attention to develop an efficient model. The key requirements that must be considered while designing a navigational system for mobile robots are origin point, obstacles, destination point, path planning, and realistic decision mechanism. However, conventional systems have limitations as slow response, long planning, large turns, and unsafe factors. Aiming at the problems, this research work presents a hybrid optimized path planning model for a mobile robot. Improved particle swarm optimization and Modified Whale optimization models are incorporated as a hybrid multi-objective approach to obtain the shortest, smoothest, and safest path for a mobile robot. Experimental results demonstrate that the proposed hybrid optimization model is suitable for mobile robot navigation for dynamic environments by obtaining a shorter, smoother, and safer path than existing algorithms.

A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms.

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.

A hybrid multi-objective optimization algorithm for software requirement problem

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.

Surrogate-Assisted Multiobjective Optimization of a Hydraulically Fractured Well in a Naturally Fractured Shale Reservoir with Geological Uncertainty

Summary Hydraulic fracturing is the most widely used technology for the commercial exploitation of shale-gas reservoirs, yet still faces high cost and development risk caused by many inestimable and uncertain geological parameters in shale reservoirs. Optimally designing the hydraulic fracturing considering the geological uncertainty is an essential topic, and also a computationally expensive problem, which still has not obtained enough investigation. Facing this urgent requirement, a novel surrogate-assisted multiobjective optimization (MOO) framework is proposed for the hydraulically fractured well optimization in shale-gas reservoirs with geological uncertainty. A shale-gas-production numerical model is illustrated using the embedded discrete-fracture model (EDFM), in which a set of stochastic power-law distributed natural-fracture (NF) models are generated to describe the geological uncertainty. To fully grasp the expected economical revenue and the development risk, the MOO strategy is applied, in which the expected value and standard deviation of the net present values (NPVs) are regarded as the objective functions; a Pareto-ranking scheme is adopted to search for the Pareto front (PF) that satisfies both objectives. In addition, a state-of-the-art hybrid multiobjective particle-swarm optimization (HMOPSO) algorithm is developed, which couples the synthetic advantages of multiobjective particle-swarm optimization (MOPSO) and the nondominated sorting genetic algorithm II (NSGA-II) and presents a higher accuracy and efficiency than both of them. Furthermore, a Gaussian-process Kriging model is adopted as the surrogate model to replace the time-consuming numerical simulation caused by the geological uncertainty; it is then integrated with the HMOPSO to construct a surrogated-assisted HMOPSO, which helps to increase the optimization efficiency and accuracy of the computationally expensive problem. Finally, the superiority of the HMOPSO against the traditional algorithms is validated using several benchmark functions, and the surrogated-assisted HMOPSO is performed into a hydraulic-fracture (HF)-well MOO example in a shale-gas reservoir with geological uncertainty. The PF is obtained, which provides a set of tradeoff solutions for designers.

Optimisation of K2CO3-based natural gas sweetening process: A hybrid Pareto and Fuzzy optimisation approach

BackgroundNatural gas is considered clean technology as compared to other fossil fuel resources. However, there is always a concern related to the carbon dioxide (CO2) removal via the gas sweetening unit. The current research focuses on enhancing the CO2 removal and reducing operating cost using a hybrid multi-objective optimisation technique, which incorporates (i) Pareto Front optimisation and (ii) Fuzzy optimisation. MethodAspen Plus simulation is performed to simulate diethanolamine (DEA) promoted potassium carbonate (K2CO3)-based natural gas sweetening unit. A total of 1000 simulation samples are conducted to study the effect of solvent concentration, flowrate, and temperature on CO2 concentration in treated gas and operating cost. These solutions are fed into Pareto Front optimisation to determine sets of Pareto-optimal solutions. Thereafter, Fuzzy optimisation is performed to identify a single most optimal solution. Significant findingsThe result showed that optimal solution with CO2 molar concentration in treated gas of 0.00006676 and operating cost of $19,495.08 h−1 can be achieved at solvent concentration of 17.5 wt.%, flowrate of 42,500 kmol h−1, and temperature of 80 °C. The energy requirement per unit of CO2 removal for the final optimised solution was 19.31% less than that of the actual plant performance.

Multi-constraint and multi-objective optimization of free-form reticulated shells using improved optimization algorithm

This paper proposes a novel multi-constraint and multi-objective optimization method to improve the integrated performance of a free-form surface reticulated shell. A geometric model was established using a non-uniform rational basis spline (NURBS) technology and bidirectional parameter line bisecting method. The height of the control points and section size of the rods were taken as optimization variables. The strain energy, economic index, and geometric integrated index were taken as optimization objectives. The sensitivity-NSGA-III hybrid multi-objective optimization algorithm (SH-NSGA-III) is developed. The SH-NSGA-III algorithm has significantly better efficiency than the NSGA-III, MOEA/D, and SPEA2 algorithms. The concept of constrained non-dominant sorting was introduced into the hybrid algorithm to process the constraints in a multi-constraint and multi-objective optimization problem. Meanwhile, an integrated multi-attribute decision-making method was used to select the optimal solution based on the Pareto optimal solution set. The multi-constraint and multi-objective optimization of a free-form surface reticulated shell was performed using the proposed method. The results demonstrated that the Pareto optimal solution set could effectively satisfy the constraints. The strain energy, economic index, and geometric integrated index were reduced by 61.4%, 36.8%, and 19.5%, respectively, and the geometric indexes were reduced by 22%, 11.5%, 20.8%, and 18.4%.

A Hybrid Method of NSGA-II and TOPSIS to Optimize the Performance of Friction Stir Extrusion

This study investigates the effect of friction stir back extrusion (FSBE) input parameters such as traverse speed, rotational speed, and wire diameter on the mechanical and microstructural properties of the produced wire. Numerous experiments were performed with different input parameters, and the grain size, hardness, and ultimate pressure strength (UPS) of each of the produced wires were investigated. In addition, to better understand the effect of input parameters, the process was simulated using the finite element method (FEM) model, and the temperature, material flow, and strain distributions in the wires were investigated. Then, using the artificial neural network (ANN), a relationship was obtained between the input parameters of the process, such as traverse speed, rotational speed, and wire diameter, with the mechanical and microstructural properties of the produced wires. This relationship was then used in a hybrid multi-objective optimization to find the optimal process parameters. Due to the higher importance of UPS in comparison to the grain size and microhardness, the weighting of 0.6, 0.2, and 0.2 were used in the TOPSIS model, and the optimum input parameters were achieved as 6 mm, 36.35 mm/min, and 456 rpm, for the traverse speed, rotational speed, and wire diameter, respectively.

Hybrid Multi-Objective Optimization Approach in Water Flooding

AbstractNon-dominated sorting genetic algorithm, second version (NSGA-II) is used as a stochastic optimization technique successfully in different engineering applications. In this study, a data-driven optimization strategy based upon evolutionary neural network (EvoNN) algorithm is developed for providing input into NSGA-II optimization. Evolutionary neural network data-driven model is built and trained using initial solutions generated by NSGA-II optimization coupled with the reservoir simulation model. Evolutionary optimization incorporated in the EvoNN strategy is applied in the trained data-driven model to generate the Pareto optimal solution, which is then used as a guiding input into NSGA-II optimization. The described method is applied in two case studies (i.e., Brugge field model and water injection pattern model). The Pareto optimal solutions obtained with data-driven model guided NSGA-II in both models show improvement in convergence and diversity of the solution. The convergence to the Pareto optimal solution has improved by 9% for case-1 (i.e., Brugge field) and by 43% for case-2 (i.e., water injection pattern model). In addition, the Pareto optimal solution obtained by the proposed hybridization has shown improvement in the water–oil ratio (WOR) up to 6% in the Brugge field and up to 97% in the water injection pattern model. This improvement can lead to wide applications in using evolutionary optimizations in real-field simulation models at acceptable computation time.

Performance enhancement of commercial ethylene oxide reactor by artificial intelligence approach

Abstract The present work emphasizes the development of a generic methodology that addresses the core issue of any running chemical plant, i.e., how to maintain a delicate balance between profit and environmental impact. Here, an ethylene oxide (EO) production plant has been taken as a case study. The production of EO takes place in a multiphase catalytic reactor, the reliable first principle-based model of which is still not available in the literature. Artificial neural network (ANN) was therefore applied to develop a data-driven model of the complex reactor with the help of actual industrial data. The model successfully built up a correlation between the catalyst selectivity and temperature with other operational parameters. A hybrid multi-objective metaheuristic optimization technique, namely ANN-multi-objective genetic algorithm (MOGA) algorithm was used to develop a Pareto diagram of selectivity versus reactor temperature. The Pareto diagram will help the plant engineers to make a strategy on what operating conditions to be maintained to make a delicate balance between profit and environmental impact. It was also found that by applying this hybrid ANN-MOGA modeling and optimization technique, for a 720 KTA ethylene glycol plant, approximately 32,345 ton/year of carbon-di-oxide emission into the atmosphere can be reduced. Along with the reduction of environmental impact, this hybrid approach enables the plant to reduce raw material cost of nine million USD per annum simultaneously.

A hybrid whale optimization algorithm with differential evolution optimization for multi-objective virtual machine scheduling in cloud computing

Virtual machine (VM) scheduling in a dynamic cloud environment is often bound with multiple quality of service parameters; therefore, it is classed as an NP-hard optimization problem. Swarm-based metaheuristics, such as the whale optimization algorithm (WOA), have gained a notable reputation for solving optimization problems. The unique bubble-net hunting behaviour and fast convergence of the algorithm led to the development of a hybrid multi-objective whale optimization algorithm-based differential evolution (M-WODE) technique to solve the VM scheduling problem. The differential evolution (DE) strategy is used to replace the randomly generated solution produced by the WOA to ensure diversity in the solution and to strengthen the local search of the M-WODE. In addition, the DE technique is applied to the Pareto front produced by the WOA to escape local optima entrapment problems. The experimental results showed that the proposed M-WODE outperformed previous algorithms in most cases on makespan and the cost trade-off.

Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm

Scientific workflow scheduling in multi-cloud computing using a hybrid multi-objective optimization algorithm

A Hybrid Multi-Objective Optimization Method Based on NSGA-II Algorithm and Entropy Weighted TOPSIS for Lightweight Design of Dump Truck Carriage

The lightweight design of vehicle components is regarded as a complex optimization problem, which usually needs to achieve two or more optimization objectives. It can be firstly solved by a multi-objective optimization algorithm for generating Pareto solutions, before then seeking the optimal design. However, it is difficult to determine the optimal design for lack of engineering knowledge about ideal and nadir values. Therefore, this paper proposes a multi-objective optimization procedure combined with the NSGA-II algorithm with entropy weighted TOPSIS for the lightweight design of the dump truck carriage. The finite element model of the dump truck carriage was firstly developed for modal analysis under unconstrained free state and strength analysis under the full load and lifting conditions. On this basis, the multi-objective lightweight optimization of the dump truck carriage was carried out based on the Kriging surrogate model and the NSGA-II algorithm. Then, the entropy weight TOPSIS method was employed to select the optimal design of the dump truck from Pareto solutions. The results show that the optimized dump truck carriage achieves a remarkable mass reduction of 81 kg, as much as 3.7%, while its first-order natural frequency and strength performance are slightly improved compared with the original model. Accordingly, the proposed procedure provides an effective way for vehicle lightweight design.

Flexible job-shop scheduling and heterogeneous repairman assignment with maintenance time window and employee timetable constraints

This work focuses on a new flexible scheduling problem in the job-shop that considers both maintenance activity and repairman competence under maintenance time window and employee timetable constraints. To deal with this problem, a hybrid multi-objective biogeography-based optimization (HMOBBO) algorithm is proposed, which has the following features: (1) a flexible decoding mechanism that considers maintenance time window and heterogeneous repairman constraints is designed; (2) three calculation methods of habitat suitability index (HSI) are defined; (3) tabu search (TS) algorithm is incorporated into the presented algorithm; and (4) a new offspring population generation mechanism is constructed. In numerical simulation, the parameter setting is firstly analyzed to ensure its robustness for different datasets via comparing the performance of each critical parameter combination. Secondly, different HSIs and migration models are separately compared through multiply running the literature instances, it is shown that fitness function 2 (the reciprocal of the sum of the normalized objective function values) and migration model 1 (constant immigration and linear emigration model) are the most suitable and steady in our experiments. Thirdly, the superiority of HMOBBO is proved by confronting with other six intelligent algorithms. Finally, through contrast variant models, the significance of considering employee timetable and repairman assignment is verified, and the benefits of the integrated optimization model/method are also demonstrated by comparing with hierarchical optimization model/method.

Multi-objective process parameters optimization of SLM using the ensemble of metamodels

Selective laser melting (SLM) is one of the most common additive manufacturing (AM) technologies that shows great potential for intelligent and clean manufacturing. The process parameters involved in the SLM process have a significant impact on the part quality and energy consumption during the printing process. Since the relationships between the process parameters and various part quality characteristics cannot be expressed explicitly, it is impractical to decide the optimal process parameters intuitively. In this work, a hybrid multi-objective optimization approach by combining the ensemble of metamodels (EM) and non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to generate optimal process parameters to improve the energy consumption, the tensile strength, and the surface roughness of the as-built parts. First, the Taguchi experiment design is adopted and the corresponding SLM experiments are conducted to obtain the experimental results. Second, the correlations between the process parameters (i.e., laser power, layer thickness, scanning speed) and the three responses are fitted using the proposed EM. The comparative results show that the prediction ability of the EM outperforms the stand-alone metamodels. Then, the NSGA-II is used to search for multi-objective Pareto optimal solutions based on the constructed EM. Finally, the verification experiments were conducted to verify the optimal results obtained by the proposed multi-objective optimization approach. Results indicate that the optimal process parameters are effective and reliable. Besides, the main effects of process parameters on the responses are analyzed. Overall, the proposed hybrid multi-objective optimization method exhibits great ability to improve the effectiveness of SLM.

Q-Managed: A new algorithm for a multiobjective reinforcement learning

<h2>Abstract</h2> Multi-objective reinforcement learning involves the use of reinforcement learning techniques to address problems with multiple objectives. To resolve this, we use a hybrid multi-objective optimization method that provides the mathematical guarantee that all policies belonging to the Pareto Front can be found. The hybridization gave rise to <i>Q-Managed</i>, which is given by the <mml:math><mml:mrow><mml:mi>ε</mml:mi><mml:mo>−</mml:mo></mml:mrow></mml:math>constraint method and the <i>Q-Learning</i> algorithm, where the first limits the environment dynamically based on the agent's learning. Thus, when a region no longer provides improvement, it becomes a constraint, preventing the agent from returning. The simplicity and its performance come from a single-policy algorithms.