High-order pseudo-random binary signals for frequency domain electromagnetic explorations

The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system’s performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter’s performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.

Particle swarm optimization is a popular nature-inspired metaheuristic algorithm and has been used extensively to solve single- and multi-objective optimization problems over the last two decades. Several local and global search strategies, and learning and parameter adaptation strategies have been included in particle swarm optimization to improve its performance over the years. Most of these approaches are observed to increase the number of user-defined parameters and algorithmic steps resulting in an increased complexity of the algorithm. This paper presents a simplified multi-objective particle swarm optimization algorithm in which the exploitation (guided) and exploration (random) moves are simplified using a detailed qualitative analysis of similar existing operators present in the real-coded elitist non-dominated sorting genetic algorithm and the particle swarm optimization algorithm. The developed algorithm is then tested quantitatively on 30 well-known benchmark problems and compared with a real-coded elitist non-dominated sorting genetic algorithm, and its variant with a simulated binary jumping gene operator and multi-objective non-dominated sorting particle swarm optimization algorithm. In the comparison, the developed algorithm is found to be superior in terms of convergence speed. It is also found to be better with respect to four recent multi-objective particle swarm optimization algorithms and four differential evolution variants in an extended comparative analysis. Finally, it is applied to a newly formulated industrial multi-objective optimization problem of a residue (bottom product from the crude distillation unit) fluid catalytic cracking unit where it shows a better performance than the other compared algorithms.

In the traditional design of a vehicle suspension system, mechanical parameters, such as spring stiffness and tire radial stiffness, are determined before optimizing the coordinates of suspension hard-points based on handling and stability. However, the mechanical parameters of vehicle suspension systems vary with repeated changes in ambient temperature and loading conditions. Consequently, the original hard-point coordinates cannot guarantee satisfactory handling and stability after long-term vehicle use. To address this issue, an A0 class vehicle with a MacPherson suspension was modeled in ADAMS/Car and was validated against the results of field tests. The relationships between the maximum absolute values of front wheel alignment parameters during parallel wheel travel analysis and the coordinates of suspension hard-points, spring stiffness, and tire radial stiffness were determined using support vector regression. Next, a multi-objective optimization function was formulated based on the interval analysis method. Finally, a novel double-loop multi-objective particle swarm optimization algorithm was designed for global optimization of hard-point coordinates. The ADAMS simulation results indicated that, compared with the vehicle with the original hard-point coordinates, the double-loop multi-objective particle swarm optimization algorithm, the traditional multi-objective particle swarm optimization algorithm, and the genetic algorithm can effectively reduce the variation ranges of front wheel alignment parameters, regardless of the values of the mechanical parameters. It also showed that the double-loop multi-objective particle swarm optimization algorithm performs better than the traditional multi-objective particle swarm optimization and the genetic algorithm with both the original and changed mechanical parameters. Except for an increase in the variation range of the caster angle by 9.24–10.69%, the variation ranges of the toe angle, camber angle, and kingpin inclination angle under various mechanical parameters using the double-loop multi-objective particle swarm optimization algorithm were observed to be reduced by 50.45–79.39%, 1.84–4.24%, and 2.11–2.96%, respectively. Last but not least, the proposed double-loop multi-objective particle swarm optimization algorithm provided better handling, stability, and ride comfort values than the traditional multi-objective particle swarm optimization algorithm and the genetic algorithm.

This paper concerns the multi-UAV task assignment problem, which is solved by a multi-objective particle swarm optimization algorithm for adaptive region partitioning. Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems, this paper establishes an improved multi-objective particle swarm optimization (MOPSO) algorithm based on the adaptive angle area division. This paper proposes a new multi-UAV task assignment model where the threat constraint is concerned. To solve this model, the algorithm first preprocesses solution spatial information, including normalization of solutions and area division of space. Further, global optimal particle selection strategy is improved based on angle of division. In order to improve the global searching ability, some infeasible solution is used. Finally in the implementation stage of the algorithm, we set multiple nodes for the trajectory of the UAVs to increase the stability of the algorithm. The simulation experiments results demonstrate that the improved algorithm can provide a flyable solution for the UAVs and achieve better convergence and diversity.

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.

For complex equipment, designers are challenged to reduce the expense while satisfying high requirements of system reliability. This article focuses on the multiobjective reliability–redundancy allocation problem for serial parallel-series systems to balance the conflicts between system reliability and design cost. The multiobjective reliability–redundancy allocation problem model for serial parallel-series systems is established with constraints on system reliability and design cost. An importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm is proposed to solve the multiobjective model effectively based on the importance measure–based harmony search and multiobjective particle swarm optimization algorithm. The performance of the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm is verified by comparison with the nondominated sorting genetic algorithm and importance measure–based multiobjective particle swarm optimization algorithm. In Experiment 1, the performance of the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm is better than that of the nondominated sorting genetic algorithm and importance measure–based multiobjective particle swarm optimization, and the importance measure–based and harmony search-based multiobjective particle swarm optimization algorithm also can get the Pareto front with better uniformity. Compared to the nondominated sorting genetic algorithm, four cases with different constraints of system reliability and design cost are considered in Experiment 2, and the importance measure–based and harmony search–based multiobjective particle swarm optimization algorithm applies to the systems with the lower system reliability constraints and the higher design cost constraints.

In recent years, multi-objective particle swarm optimisation (MOPSO) algorithm has been paid more attention. One of its indispensable structures is the maintenance and update mechanism of the repository. The existing mechanisms are relatively simple, and most of them are based on the crowding distance sorting strategy, and not conducive to the distribution and accuracy of the algorithms. The paper innovated this mechanism and proposed an adaptive multi-objective particle swarm optimisation algorithm to streamline repository based on fitness distance (FDMOPSO). Both the concept of fitness distance and the corresponding improve methods of mutation mechanism and adaptive mechanism was proposed. The algorithm itself was tested using benchmarks. The results show that the proposed application of fitness distance had a better improvement on the convergence and distribution. Compared with other algorithms, the FDMOPSO algorithm had the best overall performance.

Multi-objective particle swarm optimization algorithm based on objective space division for the unequal-area facility layout problem

Measuring the convergence and diversity of CDAS Multi-Objective Particle Swarm Optimization Algorithms: A study of many-objective problems

In this paper, a multi-objective particle swarm optimization algorithm based on the relative distance strategy is proposed to realize hydrodynamic coefficient identification, which can improve the accuracy of parameter identification without determining the weight manually. Firstly, single-objective particle swarm optimization (PSO) is used to identify the parameters of the model with a low coupling degree. Then, the parameters of the strongly coupled sway and yaw motion model are regarded as multi-objective, and a set of unbiased Pareto optimal solutions are obtained by the multi-objective particle swarm optimization (MOPSO) algorithm. Then the distance function of the Pareto solution is constructed to determine the parametric solution of the model. Finally, experimental identification is carried out by using the data set collected from the real ship. Experimental results show that the accuracy of model parameters obtained by this identification method is about 91.6%, and there is no need to set the weighting coefficient.

The huge computational overhead is the main challenge in the application of community based optimization methods, such as multi-objective particle swarm optimization and multi-objective genetic algorithm, to deal with the multi-objective optimization involving costly simulations. This paper proposes a Kriging metamodel assisted multi-objective particle swarm optimization method to solve this kind of expensively black-box multi-objective optimization problems. On the basis of crowding distance based multi-objective particle swarm optimization algorithm, the new proposed method constructs Kriging metamodel for each expensive objective function adaptively, and then the non-dominated solutions of the metamodels are utilized to guide the update of particle population. To reduce the computational cost, the generalized expected improvements of each particle predicted by metamodels are presented to determine which particles need to perform actual function evaluations. The suggested method is tested on 12 benchmark functions and compared with the original crowding distance based multi-objective particle swarm optimization algorithm and non-dominated sorting genetic algorithm-II algorithm. The test results show that the application of Kriging metamodel improves the search ability and reduces the number of evaluations. Additionally, the new proposed method is applied to the optimal design of a cycloid gear pump and achieves desirable results.

Effective fusion method of remote sensing multispectral and panchromatic image must ensure maximizations of spectrum and space information. Using the fusion algorithm framework with combining HIS transformation and wavelet transform (HIS-wavelet), in this paper we propose a new method to extract high frequency coefficients and a new multispectral and panchromatic image fusion method by using multi-objective particle swarm optimization (MOPSO) algorithm. According to the physical characteristics that the edge information in the high frequency has the nature of direction and noise points in the high frequency are generally isolated, a high frequency coefficient extraction method based on Gauss first order differential is proposed. The final resulting image is optimally combined by two images obtained by using different fusion rules in HIS-wavelet. Multiple fusion evaluation indicators are used as object functions and the MOPSO algorithm is used to find the optimal weights. The experiments on TM multi-spectral image and SPOT panchromatic image are carried out. Experimental results demonstrate that the improved method has a better improvement in spectral and spatial information. At the same time, the resulting image which is obtained using MOPSO algorithm has obvious advantages in retaining the spectral information and the spatial information is also greatly improved.

PurposeMulti-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.Design/methodology/approachIn this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.FindingsIn order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.Originality/valueIn order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.

In this paper, a modified Competitive Mechanism Multi-Objective Particle Swarm Optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume.

In multi-objective particle swarm optimization (MOPSO) algorithms, improving the diversity of solutions is very difficult yet an important problem. In this paper, a new MOPSO algorithm of searching the Pareto-optimal solution is introduced, called multi-objective particle swarm optimization algorithm based on self-update strategy (SU-MOPSO). The mainly strategy of SU-MOPSO is that improving the diversity of each particle local best position (usually called pbest) to satisfy the swarm update's needs, and fundamentally enhances the diversity of Pareto set by rising the candidate quantity. The proposed SU-MOPSO algorithm has been compared with ES-MOPSO algorithm. The results demonstrate that the SU-MOPSO algorithm has gained better convergence with even distributing and diversity of Pareto set.