Search Results (26)

Post-disaster rescue scenarios often involve complex and variable terrains, imposing heterogeneous mobility requirements on different transport modes. Single-type vehicles face challenges in independently completing comprehensive rescue tasks. This study addresses the critical problem of coordinating heterogeneous aerial and ground vehicles to collaboratively plan relay rescue routes. To tackle the NP hard multi-terrain, multi-vehicle, and multi-route path planning problem, we propose a New Adaptive Multi-Strategy Particle Swarm Optimization algorithm (AMS-PSO-NEW). The algorithm features a synergistic integration of differential evolution’s multi-strategy mutation, SHADE-based adaptive parameter control, population diversity monitoring with restart mechanisms, and multi-level local search. A sequential hybrid mechanism is designed in which DE-generated trial vectors serve as reference positions for PSO velocity updates, enabling balanced global exploration and local exploitation. By leveraging adaptive parameter tuning, success history memory, and diverse population maintenance, AMS-PSO-NEW effectively overcomes premature convergence and low accuracy issues typical in discrete combinatorial optimization using traditional PSO, achieving a balanced global exploration and local exploitation. Performance validation is conducted over six rescue scenarios varying in scale and complexity, benchmarking AMS-PSO-NEW against nine algorithms: PSO, GA, NSGA-II, GWO, DE, ABC, CS, Q-learning, and MIP. Results demonstrate superior performance across four metrics (rescue success rate, average rescue time, total cost, and fairness), with significant improvements in high-complexity environments. Full article

High-speed laser cladding shows significant potential for application in the field of high-performance surface hardening due to its low heat input and high cladding efficiency. However, the pool solidification time is significantly reduced at high scanning speeds, resulting in a narrower process window and making it more difficult to ensure coating formation stability and control performance. Therefore, this study employed high-speed laser cladding technology to prepare FeCrNiB alloy coatings, and systematically conducted research on process parameter optimization and friction properties. Firstly, the response surface method (RSM) was used to establish quantitative relationship models between laser power, scanning speed, and powder feed rate and the dilution ratio, forming coefficient, and microhardness. Then, the hybrid differential evolution and NSGA-II algorithm (DE-NSGA-II) was employed for multi-objective optimization. Finally, a systematic analysis was conducted on the friction and wear properties of the coatings produced under the optimal process parameters. The results indicate that the interaction between laser power and scanning speed has a significant impact on the dilution behavior of the coating, while the coupling between scanning speed and powder feed rate governs the formation characteristics and microhardness evolution of the coating. The experiment verified that the prediction error for the optimal parameters is controlled within 5%, demonstrating good engineering applicability. Further analysis indicates that grain refinement and the formation of strengthening phases in the optimal coating are the key mechanisms behind the significant improvement in hardness and wear resistance, and the coating primarily exhibits a mild abrasive wear mechanism. This work realizes the multi-objective optimization of the high-speed laser cladding process via RSM and DE-NSGA-II algorithm, which provides a novel and efficient method for parameter optimization and engineering application of high-speed laser cladding. Full article

This study investigates the turning performance of additive-manufactured polymer-based composite, with particular emphasis on the resulting dimensional error (DE) and surface roughness Ra. Cutting speed, feed rate, and depth of cut were selected as continuous process variables. Subsequently, regression-based modeling was applied to the experimental data, resulting in predictive models with a coefficient of determination (R²) equal to 96.35% and 92.88% for the DE and Ra, respectively. The analysis indicated that depth of cut and cutting speed accounted for more than 86% of the DE model’s explanatory power, while cutting speed, feed and depth of cut contributed approximately 90% to the Ra model. To further evaluate process performance, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to determine the Pareto-optimal solutions that simultaneously minimize the dimensional error and the surface roughness. It was found that the optimal solutions are generated with a cutting speed between 120 m/min and 180 m/min, depth of cut below 0.52 mm and feed ranging from 0.05 mm/rev to 0.10 mm/rev. Finally, additional validation experiments confirmed the reliability of the proposed models, yielding mean absolute prediction errors between the measured and estimated values equal to 3% for the dimensional error and 4.8% for the surface roughness. Full article

Appointment-based lock scheduling can mitigate congestion at inland ship locks, but the inherent uncertainty in vessel arrivals frequently causes severe schedule degradation, disrupting the original lockage plans. To address this challenge, we develop an optimization framework that quantifies arrival-time uncertainty using a deep ensemble to generate generates reliable prediction intervals, and embeds a rescheduling mechanism for missed appointments within a multi-objective model. The model is solved with a hybrid heuristic that combines Differential Evolution, Variable Neighborhood Search, and Non-dominated Sorting Genetic Algorithm II (DE–VNS–NSGA-II). Compared to conventional evolutionary techniques, hybrid Particle Swarm Optimization (PSO) approaches, and recent advanced algorithms (GSAA-RL and ADEA-KC), the proposed algorithm effectively overcomes premature convergence in highly constrained discrete scheduling spaces by leveraging DE for robust global exploration and VNS for deep local refinement. In simulations with 143 vessels, the approach reduced average waiting time by 18.51% (28.63 h to 23.33 h), lowered the schedule adjustment rate by 9.02% (0.331 to 0.301), and decreased lock-utilization loss by 5.06% (0.413 to 0.392) relative to a standard baseline. The results demonstrate more stable schedules and more efficient use of lock capacity under uncertainty, providing a data-driven decision-support tool for lock operators to dynamically mitigate disruptions and reallocate passage quotas at inland navigation hubs. Full article

To address the uneven distribution of the Pareto front and insufficient convergence in multi-UAV task allocation, this paper proposes GrEAPSO, an improved algorithm that hybridizes Particle Swarm Optimization (PSO) with Genetic Algorithm (GA). GrEAPSO balances exploitation and exploration through grid partitioning, adopts a dual-encoding scheme coupled with crossover and mutation to enhance population diversity, and employs a grid-based environmental selection mechanism to improve the uniformity of the Pareto set. After initialization, the algorithm iteratively performs a PSO-based local search, genetic crossover and mutation, and grid-based environmental selection. The offspring and parent populations are then merged, and the archive set is updated accordingly. Across three military UAV task-allocation scenarios (small, medium, and large), GrEAPSO is benchmarked against MOPSO, NSGA-II/III, MOEA/D-DE, RVEA, IBEA, MOMVO, and MaOGOA. All experiments use a population size of 100. Its reference point is undominated and dominates some competitors, with median gains of 55.78% in hypervolume and 8.11% in spacing. Finally, the sensitive analysis further indicates that dividing the objective space into 15–20 grids offers the best trade-off between search breadth and solution distribution. Full article

Traditional centralized highway energy systems exhibit significant resilience shortcomings in the face of climate change mitigation requirements and increasingly frequent extreme weather events. Meanwhile, prevailing microgrid control strategies remain predominantly focused on economic optimization under normal conditions, lacking the flexibility to address dynamic risks or the interdependencies between transportation and power systems. This study proposes an adaptive, risk-driven control framework that holistically coordinates power generation infrastructures, microgrids, demand-side loads, energy storage systems, and transport dynamics through continuous risk assessment. This enables the system to dynamically shift operational priorities—from cost-efficiency in stable periods to robustness during emergencies. A multi-objective optimization model is established, integrating infrastructure resilience, operational costs, and traffic impacts. It is solved using an enhanced evolutionary algorithm that combines the non-dominated sorting genetic algorithm II with differential evolution (NSGA-II-DE). Extensive simulations under extreme weather scenarios validate the framework’s ability to autonomously reconfigure operations, achieving 92.5% renewable energy utilization under low-risk conditions while elevating critical load assurance to 98.8% under high-risk scenarios. This strategy provides both theoretical and technical guarantees for securing highway renewable energy system operations. Full article

The number of high-rise residential buildings in China has a large base and rapid growth, with huge energy-saving potential. Most of the existing research focuses on the use of renewable energy to reduce energy consumption and optimize energy systems. When optimizing the renewable energy system configuration of residential buildings for solar-air source heat pump systems, the optimization algorithm and the setting of parameter ranges will have an impact on the optimization results. Therefore, to make up for the shortcomings of a single optimization process, this study proposes a joint solution based on simulations and multi-stage multi-objective optimization to improve the energy efficiency of the system and maximize economic benefits. This method was applied to perform energy consumption and economic optimization analyses for typical high-rise residential buildings in four cities in China (Shanghai, Nanjing, Wuhan, Chongqing) characterized by hot summers and cold winters. First, DeST software is used to model and calculate the building load. Then, TRNSYS software is used to establish a system simulation model. Next, the GenOpt program and the Hooke–Jeeves algorithm are used to perform the first stage of optimization with the lowest annual cost value as the objective function. Finally, MATLAB software and the NSGA-II algorithm are used to perform the second stage of optimization with the lowest annual cost value and the highest system energy efficiency ratio as the objective function, respectively. Moreover, the TOPSIS method is used to evaluate and sort the Pareto optimal solution sets to obtain the optimal decision solution. Overall, the two-stage optimization of the solar-air source heat pump system brings multiple benefits and a more significant improvement in overall performance compared to a single-stage optimization. In terms of energy utilization efficiency, the tilt and azimuth adjustments in the first stage allow the collectors to be better oriented towards the sun and to absorb solar energy more fully. This helps to improve the energy utilization efficiency of the system. For the economy of the system, the increase in the collector area and the reduction in the heat production of the air source heat pump in the second stage, as well as the increase in the volume of the water tank, have combined to reduce the operating costs of the system and improve its economy. Results demonstrate that the proposed two-stage optimization significantly improves the overall performance of the solar-air source heat pump system across all four cities, providing a robust framework for sustainable urban residential energy systems. This is a positive aspect for sustainability and environmental friendliness. Taken together, the two-stage optimization improves the performance of the system in a more comprehensive manner compared to the single-stage optimization. Full article

Hydro power has provided significant economic benefits to society due to its cleanliness and convenience. As the number of hydropower stations has increased, many serious ecological issues have also emerged. This study uses Wan’an Reservoir as its research object and investigates single–multi-objective optimal operation and decision-making regarding reservoirs for ecology-oriented operation, to meet ecological water demand and seek the optimal operation schemes for energy generation and ecological benefits. The full-process research is conducted based on the “objective-modeling constraint optimization scheme decision-making” framework. The Mann–Kendall test and ordered clustering method were used to diagnose the hydrological variation in the basin. Based on this, a hierarchical and phased ecological flow process was derived. The objectives were defined according to the flow process, and optimal operation models were constructed. The differential evolution algorithm (DE) and improved non-dominated sorting genetic algorithm-II (NSGA-II) were used to solve the models. A non-fitting curve method was used to determine the approximate inflection point of the Pareto front curve, and the curve was fitted linearly according to the approximate inflection point to obtain the conversion formula between the objectives. Based on the coefficient of variation and Mahalanobis distance, a new multi-attribute decision-making method for reservoir operation, CV-ITOPSIS, was constructed by improving the traditional TOPSIS. The results show that: (1) There is a piecewise linear contradiction between energy generation and ecological objectives, and the contradiction intensifies with an increase in incoming water frequency. (2) Before the approximate inflection point, the head significantly influences the conversion rate from the energy generation to ecology, while the discharge flow is the major influencing factor after the inflection point. The inflection point and the formula for the piecewise straight line can reveal the conversion law between the two objectives. (3) CV-ITOPSIS considers the degree of differentiation of index data and fully considers the correlation between indicators while retaining the good evaluation performance of the traditional method. It recommends the optimal benefit scheme for a multi-objective non-inferior solution set. The research results provide a theoretical foundation and decision support for the optimal ecological operation of the Ganjiang River Basin. Full article

In this study, the multi-mode resource-constrained multi-project scheduling problems (MMRCMPSPs) considering supply management and sustainable approach in the construction industry under uncertain conditions have been investigated using evidence theory to mathematical modeling and solving by multi-objective optimization algorithms. In this regard, a multi-objective mathematical model has been proposed, in which the first objective function aims to maximize a weighted selection of projects based on economic, environmental, technical, social, organizational, and competitive factors; the second objective function is focused on maximizing profit, and the third objective function is aimed at minimizing the risk of supply management. Moreover, various components, such as interest rates, carbon penalties, and other implementation limitations and additional constraints, have also been considered in the modeling and mathematical relationships to improve the model’s performance and make it more relevant to real-world conditions and related issues, leading to better practical applications. In the mathematical modeling adopted, the processing time of project activities has been considered uncertain, and the evidence theory has been utilized. This method can provide a flexible and rational approach based on evidence and knowledge in the face of uncertainty. In addition, to solve the proposed multi-objective mathematical model, metaheuristic optimization algorithms, such as the differential evolution (DE) algorithm based on the Pareto archive, have been used, and for evaluating the results, the non-dominated sorting genetic algorithm II (NSGA-II) has also been employed. Furthermore, the results have been compared based on multi-objective evaluation criteria, such as quality metric (QM), spacing metric (SM), and diversity metric (DM). It is worth noting that to investigate the performance and application of the proposed model, multiple evaluations have been conducted on sample problems with different dimensions, as well as a case study on residential apartment construction projects by a contracting company. In this respect, the answers obtained from solving the model using the multi-objective DE algorithm were better and superior to the NSGA-II algorithm and had a more favorable performance. Generally, the results indicate that using the integrated multi-objective mathematical model in the present research for managing and scheduling multi-mode resource-constrained multi-project problems, especially in the construction industry, can lead to an optimal state consistent with the desired objectives and can significantly improve the progress and completion of projects. Full article

In the field of hydrological model parameter uncertainty analysis, sampling methods such as Differential Evolution based on Monte Carlo Markov Chain (DE-MC) and Shuffled Complex Evolution Metropolis (SCEM-UA) algorithms have been widely applied. However, there are two drawbacks which may introduce bad effects into the uncertainty analysis. The first disadvantage is that few optimization algorithms consider the physical meaning and reasonable range of the model parameters. The traditional sampling algorithms may generate non-physical parameter values and poorly simulated hydrographs when carrying out the uncertainty analysis. The second disadvantage is that the widely used sampling algorithms commonly involve only a single objective. Such sampling procedures implicitly introduce too strong an “exploitation” property into the sampling process, consequently destroying the diversity property of the sampled population, i.e., the “exploration” property is bad. Here, “exploitation” refers to using good already-existing solutions and making refinements to them, so that their fitness will improve further; meanwhile, “exploration” denotes that the algorithm searches for new solutions in new regions. With the aim of improving the performance of uncertainty analysis algorithms, in this research, a constrained multi-objective intelligent optimization algorithm is proposed that preserves the physical meaning of the model parameter using the penalty function method and maintains the population diversity using a Non-dominated Sorted Genetic Algorithm-II (NSGA-II) multi-objective optimization procedure. The representativeness of the parameter population is estimated on the basis of the mean and standard deviation of the Nash–Sutcliffe coefficient, and the diversity is evaluated on the basis of the mean Euclidean distance. The Chengcun watershed is selected as the study area, and uncertainty analysis is carried out. The numerical simulations indicate that the performance of the proposed algorithm is significantly improved, preserving the physical meaning and reasonable range of the model parameters while significantly improving the diversity and reliability of the sampled parameter population. Full article

Aiming at the problem of multimodal transport path planning under uncertain environments, this paper establishes a multi-objective fuzzy nonlinear programming model considering mixed-time window constraints by taking cost, time, and carbon emission as optimization objectives. To solve the model, the model is de-fuzzified by the fuzzy expectation value method and fuzzy chance-constrained planning method. Combining the game theory method with the weighted sum method, a cooperative game theory-based multi-objective optimization method is proposed. Finally, the effectiveness of the algorithm is verified in a real intermodal network. The experimental results show that the proposed method can effectively improve the performance of the weighted sum method and obtain the optimal multimodal transport path that satisfies the time window requirement, and the path optimization results are better than MOPSO and NSGA-II, effectively reducing transportation costs and carbon emissions. Meanwhile, the influence of uncertainty factors on the multimodal transport route planning results is analyzed. The results show that the uncertain factors will significantly increase the transportation cost and carbon emissions and affect the choice of route and transportation mode. Considering uncertainty factors can increase the reliability of route planning results and provide a more robust and effective solution for multimodal transportation. Full article

Large high-clearance sprayers are widely used in the field of plant protection due to their high work efficiency. Influenced by the characteristics of a large ground clearance, fast driving speed and constantly changing sprung mass, how to solve the contradiction between the vibration reduction performance of a large sprayer and the friendliness of farmland roads has become a current research hotspot. In order to improve the driving performance of the sprayers, the design, optimization and verification scheme of the hydro-pneumatic suspension of a large sprayer based on the improved NSGA-II algorithm was completely constructed in this study. The hydro-pneumatic suspension system of a sprayer was mainly designed and a real-time time-varying model under field road excitation was established. The NSGA-II algorithm was improved by introducing the adaptive crossover operator and DE mutation operator, and a real-time interactive interface between the time-varying model was established for multi-objective optimization. Finally, system simulation analysis was conducted and a vibration test bench was built for experimental verification. The results show that vibration reduction indicators improved by 19.4%, 10.7% and 4.0%, respectively, compared with those before optimization. The performance of the designed hydro-pneumatic suspension was better than that of the ordinary suspension. Full article

Optimal synthesis of distillation sequence is a complex problem in chemical processes engineering, which involves process structure optimization and operation parameters optimization. The study of the synthesis of distillation sequence is a crucial step toward improving the efficiency of chemical processes and reducing greenhouse gas emissions. This work introduced the concept of binary tree to encode the distillation sequence. The performance of the six evolutionary algorithms was evaluated by solving a 14-component distillation sequence synthesis problem. The best algorithm was used to optimize the operation parameters of a triple-column distillation process. The total annual cost and CO₂ emissions were considered as the metrics to evaluate the performance of triple-column distillation processes. As a result, NSGA-II-DE was found to be the best one of the six tested evolutionary algorithms. Then, NSGA-II-DE was applied to the distillation sequence optimization to find the best operating parameters, which led to a significant reduction in CO₂ emission and total annual costs. Full article

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarm-intelligence-based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal. Full article

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems. Full article