Multiple Objective Optimization Research Articles

A reinforcement learning-driven adaptive decomposition algorithm for multi-objective hybrid seru system scheduling considering worker transfer

The application of hybrid seru manufacturing in real-world production often necessitates the implementation of a worker transfer strategy to overcome limitations in skill levels and realize the co-optimization of production efficiency and worker workload. Therefore, this study focuses on hybrid seru system scheduling considering worker transfer (HSSWT) to minimize both the makespan and the total labor time. The presence of multiple optimization objectives and coupled subproblems results in a considerably large search space, which poses challenges that existing optimization frameworks cannot effectively address. Thus, we propose a reinforcement learning-driven adaptive decomposition algorithm (RLADA) to address this issue. First, to minimize the search space, the HSSWT is decomposed into two subproblems, each utilizing distinct search strategies to explore specific areas. Elite-driven population evolution and a lower bound-guided greedy search are proposed to solve the individual subproblems. Second, a deep Q-network has been developed to adaptively select the subproblem to optimize in each generation by dynamically dividing the objective space. Finally, to narrow the search space, the lower bound is used to filter out unpromising solutions, thereby avoiding invalid searches. Additionally, the lower bound is utilized to determine the search depth according to the optimization margin, helping to conserve computing resources. Extensive comparative results demonstrate the effectiveness of the specialized designs, and the RLADA outperforms state-of-the-art algorithms in terms of solution convergence and diversity when addressing the HSSWT.

How to Achieve Comprehensive Carbon Emission Reduction in Ports? A Systematic Review

Under the mounting pressure to make changes to become more environmentally friendly and sustainable, port authorities have been exploring effective solutions to reduce CO2 emissions. In this regard, alternative fuels, innovative technology, and optimization strategies are key pathways for ports to transition toward a low-carbon pattern. In this review work, the current development status and characteristics of renewable and clean energy in ports were meticulously analyzed. The CO2 emission reduction effects and limitations of port microgrids, carbon capture, and other technological operations were thoroughly examined. Lastly, the emission reduction optimization strategies ports could adopt under different scenarios were evaluated. The research findings showed that (1) combining the characteristics of the port and quantifying the properties of different renewable energy sources and low-carbon fuels is extremely necessary to select suitable alternative energy sources for port development; (2) technological advancements, multi-party interests, and policy impacts were the primary factors influencing the development of emission reduction technology methods; and (3) the coordinated optimization of multiple objectives in cross-scenarios was the main direction for ports to achieve sustainable development. This study provides theoretical guidance to ports that are transitioning to a greener pattern, as well as pointing out future research directions and development spaces for researchers.

Analysis and multi-objective optimization design of sinusoidal DSEM with two different excitation modes based on Gray-Fuzzy-Taguchi method

The nonlinearity of sinusoidal doubly salient electro-magnetic machine (SDSEM) leads to the fuzzy optimization range of its design parameters. And there is a conflict problem in multiple optimization objectives. Therefore, the multi-objective optimization of SDSEM with two different excitation modes is carried out based on the Gray-Fuzzy-Taguchi method. Firstly, the proposed sinusoidal method of square wave back electromotive force (EMF) phase modulation is introduced. And the key parameters to improve the symmetry and sinusoidal degree of the back EMF waveform are pointed out. Then, the sensitivity of key parameters is analyzed and the signal-to-noise (S/N) ratio is calculated by Taguchi orthogonal test. Secondly, the S/N ratio is linearly normalized by grey correlation analysis. Then, fuzzy reasoning is used to synthesize multiple performance indexes of the motor into a multiple performance characteristics index (MPCI) value, and the optimal scheme is obtained. Finally, the optimized SDSEM with concentrated magneto-motive forces (SDSEM-CF) and SDSEM with distributed magneto-motive forces (SDSEM-DF) are simulated and compared. The SDSEM-DF prototype is developed and verified by experiments. The results show that the no-load back EMF of the prototype has a good sinusoidal degree. Based on vector control, the sinusoidal drive of DSEM is realized without increasing the control complexity, which greatly reduces the torque ripple of the flux reluctance motor.

The Multiobjective Control Based on Tolerance Optimization in a Multienergy System

To address the issue of multiobjective control in multienergy systems with diverse operational objectives, a two-stage optimization framework based on expected point tolerance has been proposed in this paper. In the first stage, a single objective function is used for optimization control to obtain the expected point of the multiobjective optimization problem. Then, in the second stage, by defining the allowable deviation between each optimization objective and the expected point, the original multiobjective optimization problem is transformed into a single objective optimization problem solution with tolerance measurement. Finally, in the simulation scene of a multienergy system, it is demonstrated that compared with the optimal results under each single objective method, the proposed method increases power line loss, maximum voltage deviation, new energy consumption, and economy by 2.22, 2.30, 1.02, and 2.45 times, respectively. Compared with the suboptimal results, the proposed method reduces power line loss by 22.26, 1.74, 1.09, and 0.97 times, respectively. Combining the shape of the Pareto frontier, it is demonstrated that the proposed method can comprehensively consider the needs of multiple power optimization objectives for forming a more reasonable and effective system optimization scheduling and also provide a new approach for solving multiobjective optimization problems.

Optimize the corrosion behaviour and mechanical properties of AISI 316 stainless steel under heat treatment and previous cold working

Abstract Improving corrosion resistance in alloys made of stainless steel is an important innovation on the petroleum trade. The effect of heat treatments (HT) and cold working on the corrosion behaviour, surface hardness, and microstructure of 316 stainless steel was investigated experimentally. The corrosion environment is seawater and crude oil. The corrosion rates (CRs) were obtained using the mean loss of weight approach, which was then optimised using the Taguchi method. The specimens used in this study are made of 316 stainless steel rod, which is first annealed to obtain the qualities of the raw material before being put through a tensile test to assess the mechanical characteristics of the metal. After cold working, the hardness test, the corrosion test utilising the lost weight method, and the microstructure test are all carried out. By performing these tests, the metal show excellent mechanical properties such as yield stress, tensile stress, and hardness; in the corrosion test, the raw metal show higher resistance in both seawater and crude oil, while in cold working and HT with cold working, samples show higher corrosion The HT samples had the lowest corrosion resistance as the cold working percentage increased. In this work, the input parameters such as ultimate corrosion media, HT and cold work (CW) are optimised utilising a multiple objective optimisation approach that uses weighted grey relational analysis. Two objectives, that are CR and Hardness (H), are simultaneously optimised. We suggested a quantitative approach to establish the weight factors of various responses for grey relational analysis called weighted grey relational analysis. The optimum input parameters were determined using weighted grey relational analysis, and the outcomes showed that HT is the most relevant parameter. Cold working has been observed in association with stress-related twinning and austenite phase deformation, resulting in fast grain splitting and the production of a microstructure that resembles a ribbon composed of austenite and ferrite.

Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN.

With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

Integrated passive design method optimized for carbon emissions, economics, and thermal comfort of zero-carbon buildings

Improving passive design methods are important for achieving zero-carbon building (ZCB) targets. Data-driven design approaches are more accurate than conventional methods for multiple-objective optimization (MOO) targets such as carbon emissions (CE), economics, and thermal comfort. In this study, a comprehensive data-driven-based PSO-SVM-NSGA-III method that assists in optimizing passive design parameters are proposed. First, the simulation results for the objectives of CE, incremental cost (IC), and time not comfort (TNC) are performed for an office building in a cold region in China using EnergyPlus and jEPlus software. Then, key influencing factors are chosen via a sensitivity analysis. Second, nonlinear mapping relationships between the passive design parameters and objectives are established with the PSO-SVM model. Compared to the BPNN, SVM, and PSO-BPNN algorithms, the PSO-SVM model exhibits superior prediction performance, with R2 values of 0.977, 0.925, and 0.903 for CE, IC, and TNC, respectively. Third, the nonlinear mapping relationships are used in the established objective functions of NSGA-II and NSGA-III. NSGA-III performed 100 iterations within 21 min and exhibited robust diversity within its population, with a hypervolume value of 0.738. Finally, the Pareto-optimal solution set is formed with the PSO-SVM-NSGA-III framework. This data-driven method offers an efficient approach for improving building performance.

STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation

Multi-task learning (MTL) has gained significant popularity in recommender systems as it enables simultaneous optimization of multiple objectives. A key challenge in MTL is negative transfer, but existing studies explored negative transfer on all samples, overlooking the inherent complexities within them. We split the samples according to the relative amount of positive feedback among tasks. Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks. Existing work commonly employs a shared-embedding paradigm, limiting the ability of modeling diverse user preferences on different tasks. In this paper, we introduce a novel Shared and Task-specific EMbeddings (STEM) paradigm that aims to incorporate both shared and task-specific embeddings to effectively capture task-specific user preferences. Under this paradigm, we propose a simple model STEM-Net, which is equipped with an All Forward Task-specific Backward gating network to facilitate the learning of task-specific embeddings and direct knowledge transfer across tasks. Remarkably, STEM-Net demonstrates exceptional performance on comparable samples, achieving positive transfer. Comprehensive evaluation on three public MTL recommendation datasets demonstrates that STEM-Net outperforms state-of-the-art models by a substantial margin. Our code is released at https://github.com/LiangcaiSu/STEM.

Attaining robust performance targets in data envelopment analysis with application to efficiency evaluation of airports

We consider a ratio-based efficiency analysis in view of achieving performance targets by Decision Making Units. The targets refer to the efficiency scores or ranks and the truth of pairwise preference relations. These outcomes can be instantiated for at least one or all feasible weights associated with inputs and outputs. We discuss Multiple Objective Optimization problems that minimize the required reductions of consumed resources or increases of produced results needed to reach the pre-defined aims. The proposed models offer great flexibility to the Decision Maker, who can indicate which factors should be modified and to which extent. The evolutionary optimization algorithm computes a set of solutions that exhibit the trade-offs between expected modifications of various factors. They can serve as the basis for interactively selecting the most preferred solution to be implemented in practice. The use of the proposed framework is illustrated in the problem of analyzing the efficiency of Polish airports.

Finite mixture models for multiscale dynamics in Chinese financial markets

A novel statistical machine learning technique for decomposing, analyzing, and modeling complex time series is presented in this paper. This methodology is based on unsupervised classification methods using finite mixtures, which have been widely used in factor analysis to extract essential information from multivariate static data in a parsimonious manner. Here, they are applied in a purely dynamic framework to create a new family of autoregressive models. The selection of the number of components and appropriate geometric template in these models is based on a set of information criteria that require solving a multiple objective optimization problem. To ensure the most adequate models are selected, the problem is augmented with a simulation instruction, providing a robust criterion for assignment. Experiments on real financial data sampled over the COVID-19 pandemic demonstrate the potential of this approach as a promising alternative to the well-known Box-Jenkins methodology. The proposed method offers flexibility, accuracy, and stability, making it suitable for a variety of applications in financial modeling and other fields where complex time series data analysis is required.

Leveraging trust for joint multi-objective and multi-fidelity optimization

In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. Multi-fidelity approaches offer potential remedies by utilizing multiple, less costly information sources, such as low-resolution approximations in numerical simulations. However, integrating these two strategies presents a significant challenge. We propose the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources. Our methodology introduces a modified multi-objective (MO) optimization policy incorporating the trust gain per evaluation cost as one of the objectives of a Pareto optimization problem. This modification enables simultaneous MOMF optimization, which proves effective in establishing the Pareto set and front at a fraction of the cost. Two specific methods of MOMF optimization are presented and compared: a holistic approach selecting both the input parameters and the fidelity parameter jointly, and a sequential approach for benchmarking. Through benchmarks on synthetic test functions, our novel approach is shown to yield significant cost reductions—up to an order of magnitude compared to pure MO optimization. Furthermore, we find that joint optimization of the trust and objective domains outperforms sequentially addressing them. We validate our findings with the specific use case of optimizing particle-in-cell simulations of laser-plasma acceleration, highlighting the practical potential of our method in the Pareto optimization of highly expensive black-box functions. Implementation of the methods in existing Bayesian optimization frameworks is straightforward, with immediate extensions e.g. to batch optimization possible. Given their ability to handle various continuous or discrete fidelity dimensions, these techniques have wide-ranging applicability in tackling simulation challenges across various scientific computing fields such as plasma physics and fluid dynamics.

Machining efficiency and geometrical accuracy on micro-EDM drilling of titanium alloy

ABSTRACT In the current study, micro-electric discharge drilling (µEDD) is conducted on a titanium grade 2 alloy using a tungsten carbide (WC) tool electrode of 496 µm diameter. Capacitance, voltage, and feed rate are selected as machining variables to conduct the experiments based on the full factorial experimental design method. Overcut, material removal rate (MRR), circularity, and hole taper are considered as performance measures. Furthermore, overall evaluation criteria (OEC) is utilized for multiple-objective optimization by allotting varying and equal weight percentages to each response measure. The results of OECs are compared with a revolutionary heuristic multi-objective genetic algorithm (MOGA), resulting in a similar optimal parametric combination and close response measures are obtained. Micrographic investigation demonstrates that at lower capacitance and voltage levels, there is less burr formation, recast layer, and circularity error. Moreover, FESEM and EDS analyses are employed to investigate the machined surface topology and elemental composition respectively.

Fairness-Aware Dynamic Ride-Hailing Matching Based on Reinforcement Learning

The core issue in ridesharing is designing reasonable algorithms to match drivers and passengers. The ridesharing matching problem, influenced by various constraints such as weather, traffic, and supply–demand dynamics in real-world scenarios, requires optimization of multiple objectives like total platform revenue and passenger waiting time. Due to its complexity in terms of constraints and optimization goals, the ridesharing matching problem becomes a central issue in the field of mobile transportation. However, the existing research lacks exploration into the fairness of driver income, and some algorithms are not practically applicable in the industrial context. To address these shortcomings, we have developed a fairness-oriented dynamic matching algorithm for ridesharing, effectively optimizing overall platform efficiency (expected total driver income) and income fairness among drivers (entropy of weighted amortization fairness information between drivers). Firstly, we introduced a temporal dependency of matching outcomes on subsequent matches in the scenario setup and used reinforcement learning to predict these temporal dependencies, overcoming the limitation of traditional matching algorithms that rely solely on historical data and current circumstances for order allocation. Then, we implemented a series of optimization solutions, including the introduction of a time window matching model, pruning operations, and metric representation adjustments, to enhance the algorithm’s adaptability and scalability for large datasets. These solutions also ensure the algorithm’s efficiency. Finally, experiments conducted on real datasets demonstrate that our fairness-oriented algorithm based on reinforcement learning achieves improvements of 81.4%, 28.5%, and 79.7% over traditional algorithms in terms of fairness, platform utility, and matching efficiency, respectively.

Multiple objective optimal design for an electrically assisted bicycle under dynamic finite element analysis

ABSTRACT Electrically assisted bicycles have become increasingly popular in modern cities. This article presents an optimization design process to enhance the strength of an electrically assisted bicycle frame (EABF) model. This process includes several techniques such as uniform design (UD), Kriging interpolation (KGI), entropy weighting analysis (EWA), gray relational analysis (GRA), and genetic algorithm (GA). The EN 15,194 test standard is used to calculate the maximum deformation of the fork in an EABF, through falling mass (FM) and falling frame (FF) impact simulations with ANSYS/LS-DYNA software. Six geometry characteristics of an EABF are selected as control factors. The UD generates multiple simulation tests, as each control factor in the design space is continuous. Dynamic finite element analysis (FEA) is conducted to determine the maximum impact deformation (ID) of each experiment in the UD. Applying a multiple objective optimization process, the best design for an EABF is obtained. The newly designed model offers a maximum improvement of 4.20% over the original design for the largest distortion. In conclusion, the multiple objective optimization technique improves the maximum ID of an EABF.

Dual-Stage Optimization Scheduling Model for a Grid-Connected Renewable Energy System with Hybrid Energy Storage

To operate the grid-connected renewable energy system economically, this study presents a dual-stage optimization scheduling model for grid-connected systems with hybrid energy storage, including day-ahead and intra-days stages. In the day-ahead stage, an economically optimal scheduling model is developed, considering the price peak-to-valley difference. This model aims to enhance the economic efficiency of the system by utilizing hybrid energy storage. In the intra-day stage, more accurate renewable energy forecasts with a shorter time scale are considered. The objectives are to minimize the curtailment rate of renewable energy and to track the day-ahead scheduling outcomes. The NSGA-II algorithm is employed for multi-objective optimization, achieving equilibrium solutions considering multiple optimization objectives. Compared to other published works, the proposed model achieves a balance between different optimization objectives, enabling the system to operate economically and stably. It provides a comprehensive approach to optimize the scheduling of grid-connected systems with hybrid energy storage by considering both economic and operational aspects. Overall, this proposed dual-stage optimization model presents a viable approach to improve economic efficiency and mitigate renewable energy curtailment in grid-connected systems. By effectively integrating renewable energy sources and optimizing their utilization, this model contributes to enhancing the sustainability and optimal operation of the power grid.

Investigation and optimization of multiple objectives by hybrid evolutionary algorithms for turning of Nimonic 80A under nano MQL environment

ABSTRACT Nimonic80A is one of the hard-to-cut superalloy materials. However, machining the same remains difficult. The present study’s objective is attained by taking surface quality and tool wear as output core attributes of machining by deploying conventional statistical approaches and meta-heuristic optimisation algorithms to find optimal input attributes. Optimal input parameters were found using ANN hybridised with meta-heuristic optimisation algorithms, considering cutting acceleration as one of the response attributes along with cutting force (Fz), flank wear (Vb) and surface roughness (Ra). In this process, nanofluids were formulated by suspensions of graphene oxide into rice bran oil as the minimum quantity lubrication (MQL). Experiments were conducted based on L27 Taguchi experimental trials, and the experimental results revealed that minimum feed rate and cutting velocity reduced the cutting force, surface roughness, and tool flank wear up to 51% during nano-MQL turning conditions compared to dry machining. Thus, an artificial neural network (ANN) was used to generate the model for each machining response, which was further interfaced with multiobjective particle swarm optimisation (MOPSO) and multiobjective mayfly algorithms (MOMA) as a hybrid algorithm for finding optimal parameters. The optimal results were compared, and ANN-MOMA was found to be better.

Efficient Multi-Objective Simulation Metamodeling for Researchers

Solving multiple objective optimization problems can be computationally intensive even when experiments can be performed with the help of a simulation model. There are many methodologies that can achieve good tradeoffs between solution quality and resource use. One possibility is using an intermediate “model of a model” (metamodel) built on experimental responses from the underlying simulation model and an optimization heuristic that leverages the metamodel to explore the input space more efficiently. However, determining the best metamodel and optimizer pairing for a specific problem is not directly obvious from the problem itself, and not all domains have experimental answers to this conundrum. This paper introduces a discrete multiple objective simulation metamodeling and optimization methodology that allows algorithmic testing and evaluation of four Metamodel-Optimizer (MO) pairs for different problems. For running our experiments, we have implemented a test environment in R and tested four different MO pairs on four different problem scenarios in the Operations Research domain. The results of our experiments suggest that patterns of relative performance between the four MO pairs tested differ in terms of computational time costs for the four problems studied. With additional integration of problems, metamodels and optimizers, the opportunity to identify ex ante the best MO pair to employ for a general problem can lead to a more profitable use of metamodel optimization.

Research on Multi-Objective Optimization on Explosion-Suppression Structure-Nonmetallic Spherical Spacers

Intense burning phenomena (fire disasters) need to be prevented in the combustible gas utilization and transportation processes to ensure industrial safety. Nonmetallic spherical spacers (NSSs) have been investigated and applied in lots of explosive atmospheres to prevent explosion execution in a confined space. In this work, a novel fuzzy-based analytic hierarchy process (FAHP) is developed to take into account the uncertainty in decision-making and effectively solve the problem of factor weight allocation in multi-objective optimization. Optimal Latin Hypercube Design (Opt LHD), Chebyshev Orthogonal Polynomials (COP), and Adaptive Simulated Annealing (ASA) were combined. A multi-objective optimization method is proposed for the structural parameter optimization problem on NSSs in order to achieve conflicting multiple-objective optimization of low displacement rate and minimal deformation. That is to say, the small volume (low displacement rate) and high explosion-suppression performance (minimal deformation) of NSSs were optimized simultaneously. The results show that, compared with the original NSS model’s deformation (2.85 mm) and displacement rate (3.63%), the optimized NSSs with weight allocation had optimized the deformation by 12.98% and displacement rate by 6.1%. Compared with the optimized design model of NSSs without weight allocation with a deformation of 2.75 mm and a displacement rate of 3.48%, the deformation has been optimized by 9.82%, and the displacement rate has been optimized by 2.0%. It was verified that the proposed method is effective. At the same time, it was verified that the suppression effect of NSSs can be enhanced by changing the shape of the NSS spacer reasonably by experimental verification.

Enhanced Solutions to Intuitionistic Fuzzy Multiobjective Linear Fractional Optimization Problems via Lexicographic Method

Optimization involving fuzzy numbers generally, and intuitionistic fuzzy numbers particularly is more and more an essential part of any computational intelligence method; and might be of interest in modeling fuzzy control systems, or carrying out a fuzzy sensitivity analysis. The recent literature includes many research papers related to both theoretical modelling, and practical implementation of fuzzy decision support systems. Fuzzy optimization is one of the main part in such decisionmaking tools. A realistic solution to a fuzzy optimization problem is always desired, and similarly, a Pareto optimal solution to a multiple objective optimization problem is always a need. In this paper we analyze the shortcomings; and eliminate the weaknesses of a solution approach from the literature proposed by Moges et al. in 2023. Firstly, Moges et al. used an accuracy function to de-fuzzify the original intuitionistic fuzzy optimization problem; then linearized the obtained crisp problem; and finally involved fuzzy goals to solve the derived multiple objective linear problem. We present two faulty key points of their approach, namely the de-fuzzification and linearization steps; prove the inappropriateness of their results; and propose some improvements. The final aim in addressing the first key point is to clarify the border between defuzzifications made in accordance to true theoretical statements, and those made for modeling reasons. The methodological improvement we propose is related to the second key point and it assures that Pareto optimal solutions - highly required in multiple objective optimization - are obtained. To illustrate our point of view, we use a numerical example from the literature, and report better numerical results derived by the improved methodology.

A Cooperative Evolutionary Computation Algorithm for Dynamic Multiobjective Multi-AUV Path Planning

Multiple autonomous underwater vehicles (AUVs) are popular for executing submarine missions, which involve multiple targets distributed in a large and complex underwater environment. The path planning of multiple AUVs is a significant and challenging problem, which determines the location of surface points for AUV launch and plans the paths of AUVs for target traveling. Most existing works model the problem as a single-objective static optimization problem. However, the target missions may change over time, and multiple optimization objectives are usually expected for decision making. Thus, this paper models the problem as a dynamic multiobjective optimization problem and proposes a cooperative evolutionary computation algorithm to provide diverse and high-quality solutions for decision makers. In the proposed method, solutions are represented using a bi-layer encode scheme, in which the first layer indicates the surface location points and the second layer represents the traveling sequences of target missions. Multiple populations for multiple objectives (MPMO) framework is adopted to efficiently solve the dynamic multiobjective AUV optimization problem. In addition, a recombination-based sampling strategy is developed to improve convergence by fusing the information of multiple populations. Once a change occurs, an incremental response strategy is adopted to generate high-quality solutions for population evolution. Based on the dataset of New Zealand bathymetry, six complex underwater scenarios are constructed with a size of 50 km×50 km×10 km and 400 target missions for tests. Experimental results show that the proposed method outperforms the state-of-the-art algorithms in terms of solution diversity and optimality.