Research on Collaborative Optimization Strategy of Railway Signal Nonlinear Control System Based on BBO Algorithm and Multi-objective Optimization

Multi-Objective Feature Selection Algorithm using Beluga Whale Optimization

In order to improve the dynamic performance during the startup process of hydropower units, while considering the efficient and stable speed increase and effective suppression of water pressure fluctuations and mechanical vibrations, optimization algorithms must be used to select the optimal parameters for the system. However, in current research, various multi-objective optimization algorithms still have limitations in terms of target space coverage and diversity maintenance in parameter optimization during the startup process of hydraulic turbines. To explore and verify the optimal algorithms and parameters for the startup process of hydraulic turbines, multiple multi-objective optimization strategies are proposed in this study. Under the condition of constructing a fine-tuned nonlinear model of the control system, this paper focuses on three key indicators: the absolute integral of the speed deviation, the absolute integral of the snail shell water pressure fluctuation, and the relative value of the maximum axial water thrust. Through comparative analysis of the multi-objective particle swarm optimization algorithm (MOPSO), variant multi-objective particle swarm optimization algorithm (VMOPSO), multi-objective sine cosine algorithm (MOSCA), multi-objective biogeography algorithm (MOBBO), multi-objective gravity search algorithm (MOGAS), and improved multi-objective particle swarm optimization algorithm (IMOPSO), the obtained optimal parameters are compared and analyzed to select the optimal multi-objective optimization strategy, and the most suitable parameters for actual working conditions are selected through a comprehensive weighting method. The results show that, compared to the local optimal solution problem caused by other optimization algorithms, the improved multi-objective optimization method significantly reduces water pressure fluctuations and mechanical vibrations while ensuring stable speed improvement, achieving better control performance. The optimization results have significant guiding significance for ensuring the smooth operation and safety of hydropower units, and provide strong support for making operational decisions.

Multi-objective optimal control of bioconversion process considering system sensitivity and control variation

Abstract. In order to improve the accuracy and efficiency of special machining for a complex surface, a 2RPU-2SPR (where R, P, U, and S stand for revolute, prismatic, universal, and spherical joints, respectively) over-constrained redundantly actuated parallel robot mechanism is proposed. And six performance evaluation indexes are established to ensure the working performance including workspace, motion/force transmission efficiency, stiffness, dexterity, energy efficiency, and the inertia coupling index. Furthermore, a collaborative optimal configuration algorithm is conducted based on an orthogonal experimental design algorithm and a multi-objective particle swarm optimization algorithm. On the basis given above, a simulation analysis of a multi-objective optimization is conducted. Compared with two traditional, intelligent optimization algorithms of a multi-objective particle swarm optimization algorithm and an orthogonal experimental design method, the improved collaborative multi-objective optimization algorithm has a better optimization effect.

In order to solve the multiobjective optimization problems efficiently, this paper presents a hybrid multiobjective optimization algorithm which originates from invasive weed optimization (IWO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), a popular framework for multiobjective optimization. IWO is a simple but powerful numerical stochastic optimization method inspired from colonizing weeds; it is very robust and well adapted to changes in the environment. Based on the smart and distinct features of IWO and MOEA/D, we introduce multiobjective invasive weed optimization algorithm based on decomposition, abbreviated as MOEA/D‐IWO, and try to combine their excellent features in this hybrid algorithm. The efficiency of the algorithm both in convergence speed and optimality of results are compared with MOEA/D and some other popular multiobjective optimization algorithms through a big set of experiments on benchmark functions. Experimental results show the competitive performance of MOEA/D‐IWO in solving these complicated multiobjective optimization problems.

Micro-time variant multi-objective particle swarm optimization (micro-TVMOPSO) of a solar thermal combisystem

Distributed optical fiber sensing for real-time downhole monitoring is an essential technology in the efficient development of Middle Eastern carbonate reservoirs, in which distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) are two frequently utilized monitoring techniques. Efficiently and accurately inversing DTS and DAS data is important in identifying key water injection channels, capitalizing on residual oil reserves, and accurately forecasting production metrics. Meanwhile, there are two aspects of challenges in inversing DTS and DAS data, the first one is the inversion algorithms developed so far lack robustness and efficiency when facing an extensive set of parameters and computationally expensive forward models. The other one is that existing inversion techniques for distributed fiber optic monitoring data rely solely on either DTS or DAS data, with no research conducted on the combined inversion of DTS and DAS data. With those in mind, a joint inversion method coupling deep learning (DL) and multi-objective optimization (MOO) algorithm called DL-MOO is proposed for simultaneous inversion DTS and DAS so as to obtain the comprehensive inversing results with reservoir parameters including reservoir permeability, water saturation, and grid well indices. The proposed DL-MOO method integrates DL and MOO to address the joint inverse problem of DTS and DAS data with an extensive set of parameters and the computationally expensive forward model. In detail, the Long Short-Term Memory auto-encoder (LSTMAE) technique effectively condenses interpretation parameter sets into compact latent vector representations to achieve the goal of reducing the dimensionality of the parameter space. Subsequently, the inversion process is conducted within the neural network's latent variable space rather than the conventional parameter space of the forward model, leading to notable enhancements in efficiency and robustness. After that, the hybrid multi-objective particle swarm optimization algorithm (HMPSO) is adopted to search and update latent variables into the forward model to obtain the Pareto front (PF) for maximum R2 of temperature profile with DTS data and the R2of frequency band extracted with DAS data. Furthermore, a case study is conducted on a horizontal injection well in the Middle East carbonate reservoir to demonstrate the superior performance of the DL-MOO method. The results indicate that the PF of the DL-MOO method matched well with the PF of the commercial software-based MOO method, which validates its effectiveness and reliability. Additionally, a series of comparison analyses among the DL-MOO method against, the DL-MOPSO (Multi-objective Particle Swarm Optimization) method and the DL-NSGA-II (non-dominated sorting genetic algorithm-II) are executed to demonstrate the remarkable enhancements in the quality of inversion results achieved by the DL-MOO method. Under the same iteration steps, the convergence and diversity of the PF the DL-MOPSO and the DL- NSGA-II method are dominated by the PF of DL-MOO method. To the best of our knowledge, this is the first time that the joint inversion of DTS and DAS data for interpreting reservoir parameters. Through the integrated inversion of DTS and DAS data, the DL-MOO method realizes the purpose of robustness and efficient interpretation of parameter sets along the wellbore direction, encompassing reservoir permeability, water saturation, and grid well indices. Moreover, the precise interpretation results attained through the DL-MOO method could substantially enhance the effectiveness and accuracy of evaluating and monitoring horizontal well performance, which holds significant importance for optimizing the development of water-flooding carbonate reservoirs with horizontal wells.

This paper deals with the problem of robust control for Uncertain Nonlinear System via fuzzy control approach. First, a fuzzy controller with robust control capability is proposed. For avoiding complex controller parameters adjustment, Multi-objective particle swarm Optimization (PSO) algorithm is adopted during the controller design. Secondly, the multi-model is employed to represent the nonlinear system with norm-bounded parameter uncertainties. Finally, a simulation example illustrates the effectiveness and the feasibility of the proposed approach.

The low efficiency and dissatisfactory chromaticity remain as important challenges on the road to the OLED commercialization. In this paper, we propose a multi-objective collaborative optimization strategy to simultaneously improve the efficiency and ameliorate the chromaticity of the stratified OLED devices. Based on the formulations derived for the current efficiency and the chromaticity Commission International de L'Eclairage (CIE) of OLEDs, an optical sensitivity model is presented to quantitatively analyze the influence of the layer thickness on the current efficiency and the CIE. Subsequently, an evaluation function is defined to effectively balance the current efficiency as well as the CIE, and a collaborative optimization strategy is further proposed to simultaneously improve both of them. Simulations are comprehensively performed on a typical top-emitting blue OLED to demonstrate the necessity and the effectivity of the proposed strategy. The influences of the layer thickness incorporated in the blue OLED are ranked based on the sensitivity analysis method, and by optimizing the relative sensitive layer thicknesses in the optical views, a 16% improvement can be achieved for the current efficiency of the OLED with desired CIE meantime. Hence, the proposed multi-objective collaborative optimization strategy can be well applied to design high-performance OLED devices by improving the efficiency without chromaticity quality degradation.

Nonlinear dynamic instability and multi-objective design optimization of the GPLR-FGP plate under biaxial impacts

Multi-objective optimisation of chemical processes via improved genetic algorithms: A novel trade-off and termination criterion

Product configuration is one of the key technologies for mass customization. Traditional product configuration optimization targets are mostly single. In this paper, an approach based on multi-objective genetic optimization algorithm and fuzzy-based select mechanism is proposed to solve the multi-objective configuration optimization problem. Firstly, the multi-objective optimization mathematical model of product configuration is constructed, the objective functions are performance, cost, and time. Then, a method based on improved non-dominated sorting genetic algorithm (NSGA-II) is proposed to solve the configuration design optimization problem. As a result, the Pareto-optimal set is acquired by NSGA-II. Due to the imprecise nature of human decision, a fuzzy-based configuration scheme evaluation and select mechanism is proposed consequently, which helps extract the best compromise solution from the Pareto-optimal set. The proposed multi-objective genetic algorithm is compared with two other established multi-objective optimization algorithms, and the results reveal that the proposed genetic algorithm outperforms the others in terms of product configuration optimization problem. At last, an example of air compressor multi-objective configuration optimization is used to demonstrate the feasibility and validity of the proposed method.

In the real world, the development model of optimization problems tends to be diversified and large scale. Therefore, optimization technologies are facing severe challenges in terms of nonlinearity, multi-dimensionality, intelligence, and dynamic programming. Multiobjective optimization problems have multiple conflicting objective functions, so the unique optimal solution is impossible to obtain when optimizing, and multiple objective values must be considered to obtain a compromise optimal solution set. When traditional optimization methods treat complex multiobjective problems, such as those with nonlinearity and high dimensionality, good optimization results are difficult to ensure or even infeasible. The evolutionary algorithm is a method that simulates the natural evolution process and is optimized via group search technology. It has the characteristics of strong robustness and high search efficiency. Inspired by the foraging behavior of bird flocks in nature, the particle swarm optimization algorithm has a simple implementation, fast convergence, and unique updating mechanism. With its outstanding performance in the single-objective optimization process, particle swarm optimization has been successfully extended to multiobjective optimization, and many breakthrough research achievements have been made in combinatorial optimization and numerical optimization. Consequently, the multiobjective particle swarm algorithm has far-reaching research value in theoretical research and engineering practice. As a meta-heuristic optimization algorithm, particle swarm optimization is widely used to solve multiobjective optimization problems. This paper summarized the advanced strategies of the multiobjective particle swarm optimization algorithm. First, the basic theories of multiobjective optimization and particle swarm optimization were reviewed. Second, the difficult problems involving multiobjective optimization were analyzed. Third, the achievements in recent years were summarized from five aspects: optimal particle selection strategies, diversity maintenance mechanisms, convergence improvement measures, coordination methods between diversity and convergence, and improvement schemes of iterative formulas, parametric and topological structure. Finally, the problems to be solved and the future research direction of the multiobjective particle swarm optimization algorithm were presented.

A competitive mechanism based multi-objective particle swarm optimizer with fast convergence

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.