Showing papers on "Multi-objective optimization published in 2022"

An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-Objective Optimization

Abstract: This article proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large-scale multiobjective and many-objective optimization problems (MaOPs). Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large-scale multiobjective and MaOPs.

Adaptive Offspring Generation for Evolutionary Large-Scale Multiobjective Optimization

Abstract: Offspring generation plays an important role in evolutionary multiobjective optimization. However, generating promising candidate solutions effectively in high-dimensional spaces is particularly challenging. To address this issue, we propose an adaptive offspring generation method for large-scale multiobjective optimization. First, a preselection strategy is proposed to select a balanced parent population, and then these parent solutions are used to construct direction vectors in the decision spaces for reproducing promising offspring solutions. Specifically, two kinds of direction vectors are adaptively used to generate offspring solutions. The first kind takes advantage of the dominated solutions to generate offspring solutions toward the Pareto optimal set (PS) for convergence enhancement, while the other kind uses those nondominated solutions to spread the solutions over the PS for diversity maintenance. The proposed offspring generation method can be embedded in many existing multiobjective evolutionary algorithms (EAs) for large-scale multiobjective optimization. Experiments are conducted to reveal the mechanism of our proposed adaptive reproduction strategy and validate its effectiveness. Experimental results on some large-scale multiobjective optimization problems have demonstrated the competitive performance of our proposed algorithm in comparison with five state-of-the-art large-scale EAs.

An efficient slime mould algorithm for solving multi-objective optimization problems

Abstract: Recently, the Slime mould algorithm (SMA) was proposed to solve the single-objective optimization problems. It is considered as a strong algorithm for its efficient global search capability. This paper presents a multi-objective optimization algorithm based on the SMA called multi-objective SMA (MOSMA). An external archive is utilized with the SMA to store the Pareto optimal solutions obtained. The archive applied to emulate the social behaviour of the slime mould in the multi-objective search space. The performance of the MOSMA is validated on the CEC’20 multi-objective benchmark test functions. Furthermore eight well-known of constrained and unconstrained test cases, four constrained engineering design problems are tested to demonstrate the MOSMA superiority. Moreover, the real-world multi-objective optimization of helical coil spring for automotive application to depict the reliability of the presented MOSMA to solve real-world problems. Over the statistical side, the Wilcoxon test and performance indicators are used to assess the effectiveness of MOSMA against six well-known and robust optimization algorithms: multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimization (MOPSO), multi-objective salp swarm algorithm (MSSA), Non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective whale optimization algorithm (MOWOA) and strength Pareto evolutionary algorithm 2 (SPEA2). The overall simulation results reveal that the proposed MOSMA has the ability to provide better solutions as compared to the other algorithms in terms of Pareto sets proximity (PSP) and inverted generational distance in decision space (IGDX) indicators.

An efficient slime mould algorithm for solving multi-objective optimization problems

Abstract: Recently, the Slime mould algorithm (SMA) was proposed to solve the single-objective optimization problems. It is considered as a strong algorithm for its efficient global search capability. This paper presents a multi-objective optimization algorithm based on the SMA called multi-objective SMA (MOSMA). An external archive is utilized with the SMA to store the Pareto optimal solutions obtained. The archive applied to emulate the social behaviour of the slime mould in the multi-objective search space. The performance of the MOSMA is validated on the CEC’20 multi-objective benchmark test functions. Furthermore eight well-known of constrained and unconstrained test cases, four constrained engineering design problems are tested to demonstrate the MOSMA superiority. Moreover, the real-world multi-objective optimization of helical coil spring for automotive application to depict the reliability of the presented MOSMA to solve real-world problems. Over the statistical side, the Wilcoxon test and performance indicators are used to assess the effectiveness of MOSMA against six well-known and robust optimization algorithms: multi-objective grey wolf optimizer (MOGWO), multi-objective particle swarm optimization (MOPSO), multi-objective salp swarm algorithm (MSSA), Non-dominated sorting genetic algorithm version 2 (NSGA-II), multi-objective whale optimization algorithm (MOWOA) and strength Pareto evolutionary algorithm 2 (SPEA2). The overall simulation results reveal that the proposed MOSMA has the ability to provide better solutions as compared to the other algorithms in terms of Pareto sets proximity (PSP) and inverted generational distance in decision space (IGDX) indicators.

Multiobjective U-Shaped Disassembly Line Balancing Problem Considering Human Fatigue Index and an Efficient Solution

Abstract: The progress of science and technology speeds up the replacement of products and produces a large number of end-of-life products. Traditional incineration causes a waste of resources and pollution to the environment. Disassembling and recycling end-of-life products are the recommended way to maximize the utilization of resources and reduce environmental pollution. Disassembly performance is affected by many factors, such as the disassembly posture of the human body, the fatigue of workers on a workstation, disassembly profit, and task precedence relationship. In this article, a mixed integer linear programming mathematical model for U-shaped layout disassembly line balancing problems is developed, in which the balance of workers’ fatigue indices is an optimization objective in addition to disassembly profits. An efficient solution to the problem that uses a collaborative resource allocation strategy of the multiobjective evolutionary algorithm is proposed. The linear programming solver CPLEX is used to verify the accuracy of the model and compared with the proposed algorithm. Experiments demonstrate that the algorithm is significantly superior to the CPLEX solver in handling large-scale cases. The proposed algorithm is also compared with two well-known algorithms, which further verifies its superiority.

A self-driving laboratory advances the Pareto front for material properties

Abstract: Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m-1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m-1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m-1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.

Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling With Dynamic Reference Point-Based Fuzzy Relative Entropy

Abstract: Energy-efficient production scheduling research has received much attention because of the massive energy consumption of the manufacturing process. In this article, we study an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness and total energy consumption simultaneously. To effectively evaluate and select solutions for a multiobjective optimization problem of this nature, a novel fitness evaluation mechanism (FEM) based on fuzzy relative entropy (FRE) is developed. FRE coefficients are calculated and used to evaluate the solutions. A multiobjective optimization framework is proposed based on the FEM and an adaptive local search strategy. A hybrid multiobjective genetic algorithm is then incorporated into the proposed framework to solve the problem at hand. Extensive experiments carried out confirm that our algorithm outperforms five other well-known multiobjective algorithms in solving the problem.

A Comprehensive Review of Control Strategies and Optimization Methods for Individual and Community Microgrids

Abstract: Community Microgrid offers effective energy harvesting from distributed energy resources and efficient energy consumption by employing an energy management system (EMS). Therefore, the collaborative microgrids are essentially required to apply an EMS, underlying an operative control strategy in order to provide an efficient system. An EMS is apt to optimize the operation of microgrids from several points of view. Optimal production planning, optimal demand-side management, fuel and emission constraints, the revenue of trading spinning and non-spinning reserve capacity can effectively be managed by EMS. Consequently, the importance of optimization is explicit in microgrid applications. In this paper, the most common control strategies in the microgrid community with potential pros and cons are analyzed. Moreover, a comprehensive review of single objective and multi-objective optimization methods is performed by considering the practical and technical constraints, uncertainty, and intermittency of renewable energies sources. The Pareto-optimal solution as the most popular multi-objective optimization approach is investigated for the advanced optimization algorithms. Eventually, feature selection and neural network-based clustering algorithms in order to analyze the Pareto-optimal set are introduced.

An Evolutionary Multitasking Optimization Framework for Constrained Multiobjective Optimization Problems

Abstract: When addressing constrained multiobjective optimization problems (CMOPs) via evolutionary algorithms, various constraints and multiple objectives need to be satisfied and optimized simultaneously, which causes difficulties for the solver. In this article, an evolutionary multitasking (EMT)-based constrained multiobjective optimization (EMCMO) framework is developed to solve CMOPs. In EMCMO, the optimization of a CMOP is transformed into two related tasks: one task is for the original CMOP, and the other task is only for the objectives by ignoring all constraints. The main purpose of the second task is to continuously provide useful knowledge of objectives to the first task, thus facilitating solving the CMOP. Specially, the genes carried by parent individuals or offspring individuals are dynamically regarded as useful knowledge due to the different complementarities of the two tasks. Moreover, the useful knowledge is found by the designed tentative method and transferred to improve the performance of the two tasks. To the best of our knowledge, this is the first attempt to use EMT to solve CMOPs. To verify the performance of EMCMO, an instance of EMCMO is obtained by employing a genetic algorithm as the optimizer. Comprehensive experiments are conducted on four benchmark test suites to verify the effectiveness of knowledge transfer. Furthermore, compared with other state-of-the-art constrained multiobjective optimization algorithms, EMCMO can produce better or at least comparable performance.

Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization

Abstract: Both objective optimization and constraint satisfaction are crucial for solving constrained multiobjective optimization problems, but the existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this article proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-the-art algorithms, especially on problems with complex feasible regions.

A self-driving laboratory advances the Pareto front for material properties

Abstract: Useful materials must satisfy multiple objectives, where the optimization of one objective is often at the expense of another. The Pareto front reports the optimal trade-offs between these conflicting objectives. Here we use a self-driving laboratory, Ada, to define the Pareto front of conductivities and processing temperatures for palladium films formed by combustion synthesis. Ada discovers new synthesis conditions that yield metallic films at lower processing temperatures (below 200 °C) relative to the prior art for this technique (250 °C). This temperature difference makes possible the coating of different commodity plastic materials (e.g., Nafion, polyethersulfone). These combustion synthesis conditions enable us to to spray coat uniform palladium films with moderate conductivity (1.1 × 105 S m-1) at 191 °C. Spray coating at 226 °C yields films with conductivities (2.0 × 106 S m-1) comparable to those of sputtered films (2.0 to 5.8 × 106 S m-1). This work shows how a self-driving laboratoy can discover materials that provide optimal trade-offs between conflicting objectives.

Multi-objective optimization of building design for life cycle cost and CO2 emissions: A case study of a low-energy residential building in a severe cold climate

Abstract: Currently, building construction and operation are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the sustainable development perspective, it is crucial to consider the impact of construction material on the achievement of life cycle benefits. This study proposed a simulation-based multi-objective optimization method to minimize both life cycle cost and CO2 emissions of buildings. We built an energy simulation model with hybrid ventilation and light-dimming control in EnergyPlus based on an operational passive residential building in a severe cold climate. Next, this investigation selected insulation thickness, window type, window-to-wall ratio, overhang depth and building orientation as design variables. The study ran parametric simulations to establish a database and then used artificial neural network models to correlate the design variables and the objective functions. Finally, we used the multi-objective optimization algorithm NSGA-II to search for the optimal design solutions. The results showed potential reductions of 10.9%–18.9% in life cycle cost and 13.5%–22.4% in life cycle CO2 emissions compared with the initial design. The results indicated that the optimization approach in this study would improve building performance. The optimal values of the design variables obtained in this study can guide designers in meeting economic and environmental targets in passive buildings.

Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep Q network

Abstract: With the extensive application of automated guided vehicles in manufacturing system, production scheduling considering limited transportation resources becomes a difficult problem. At the same time, the real manufacturing system is prone to various disturbance events, which increase the complexity and uncertainty of shop floor. To this end, this paper addresses the dynamic flexible job shop scheduling problem with insufficient transportation resources (DFJSP-ITR) to minimize the makespan and total energy consumption. As a sequential decision-making problem, DFJSP-ITR can be modeled as a Markov decision process where the agent should determine the scheduling object and allocation of resources at each decision point. So this paper adopts deep reinforcement learning to solve DFJSP-ITR. In this paper, the multiobjective optimization model of DFJSP-ITR is established. Then, in order to make agent learn to choose the appropriate rule based on the production state at each decision point, a hybrid deep Q network (HDQN) is developed for this problem, which combines deep Q network with three extensions. Moreover, the shop floor state model is established at first, and then the decision point, generic state features, genetic-programming-based action space and reward function are designed. Based on these contents, the training method using HDQN and the strategy for facing new job insertions and machine breakdowns are proposed. Finally, comprehensive experiments are conducted, and the results show that HDQN has superiority and generality compared with current optimization-based approaches, and can effectively deal with disturbance events and unseen situations through learning.

Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling With Dynamic Reference Point-Based Fuzzy Relative Entropy

Abstract: Energy-efficient production scheduling research has received much attention because of the massive energy consumption of the manufacturing process. In this article, we study an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness and total energy consumption simultaneously. To effectively evaluate and select solutions for a multiobjective optimization problem of this nature, a novel fitness evaluation mechanism (FEM) based on fuzzy relative entropy (FRE) is developed. FRE coefficients are calculated and used to evaluate the solutions. A multiobjective optimization framework is proposed based on the FEM and an adaptive local search strategy. A hybrid multiobjective genetic algorithm is then incorporated into the proposed framework to solve the problem at hand. Extensive experiments carried out confirm that our algorithm outperforms five other well-known multiobjective algorithms in solving the problem.

Pareto multi-objective optimization of tandem cold rolling settings for reductions and inter stand tensions using NSGA-II.

Abstract: In this paper, multi-objective optimization of tandem cold rolling settings for reductions and inter-stand tensions using NSGA-II and Pareto-optimal front are investigated. In this multi-objective optimization, the total power consumption and uniform power distribution are suggested as objective functions, and reduction thicknesses in each stand and inter stand tensions were selected as problem decision variables. Analytical formulations are introduced to determine the rolling forces and power based on the Stone approach. Then, the main variables of the optimization problem, objective functions, linear and nonlinear constraints, are defined. Moreover, some empirical constraints are introduced regarding the practical limitations of cold rolling equipment and the mechanical properties of the material. At first, considering the conditions of a practical tandem rolling line, single-objective optimization is performed separately, and finally, NSGA-II was used for multi-objective optimization. Compared to the initial setting of the rolling line, the obtained single objective schedules have better performance. Moreover, the multi-objective results based on the Pareto-optimal front are investigated, and an optimized setting for rolling schedule has been suggested. Using this proposed schedule the total power consumption is reduced by more than 11% comparing to the initial setting and more uniform power distribution has been obtained in rolling stands. The normalized reductions calculated from this investigation are compared with numerical and experimental results found in the literature and the similarity was observed in the pattern of thickness reduction distribution.

Deep Belief Network Enabled Surrogate Modeling for Fast Preventive Control of Power System Transient Stability

Abstract: The widely used transient stability-constrained optimal power flow (TSC-OPF) method for power system preventive control is very time-consuming and thus not applicable for large-scale systems. This article proposes a new deep learning-enabled surrogate model that can significantly improve computational efficiency while maintaining high accuracy. To achieve that, the deep belief network (DBN) is strategically integrated with the reference-point-based nondominated sorting genetic algorithm (NSGA-III) to develop a new preventive control framework. The DBN allows us to identify the mapping relationship between the transient stability index and system operational features. The identified functional mapping relationship is further used as the surrogate to connect the DBN results with TSC-OPF for preventive control. The integrated NSGA-III and surrogate model enable the multiobjective optimization to consider various constraints and objectives, such as minimization of costs of generation dispatch cost and load shedding while maintaining the system stability. Extensive simulation results on several IEEE test systems show that the proposed method can achieve highly efficient control solutions and outperform other alternatives in terms of computational efficiency and economic benefits.