A Hybrid Particle Swarm Optimization (PSO)‐Simplex Algorithm for Damage Identification of Delaminated Beams

MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Non-convex optimization problems are prevalent in scientific and engineering applications, often characterized by multiple local minima that challenge standard optimization techniques. Global optimization methods, such as Genetic Algorithms (GA) (1992) [4] and Particle Swarm Optimization (PSO) (1995) [6], provide robust global exploration but tend to converge slowly. Conversely, local search methods, such as Newton’s method (2004) [1], (2006) [7], and the Conjugate Gradient (CG) (1964) [3] method, offer rapid convergence but are susceptible to local minima. In this paper, we present four hybrid optimization algorithms that integrate the exploratory strength of global algorithms with the refinement ability of local methods. The proposed hybrids, Genetic Algorithms (GA) + Newton, GA + CG, PSO + Newton, and PSO + CG, are evaluated against their standalone counterparts on standard benchmark functions (2013) [5], including Rosenbrock, Rastrigin, Ackley, and Himmelblau. This work builds upon our previous research on the hybridization of two global search algorithms, the Nelder–Mead (simplex) algorithm and the Bat algorithm, which resulted in the Hybrid Simplex Bat Algorithm (HSBA) [2], recently Published. Results demonstrate that the hybrid approaches consistently outperform traditional methods in terms of accuracy, convergence rate, and computational efficiency.

Instrumented indentation test is a promising non-destructive method to determine mechanical properties. This paper proposes a new approach to determine the plastic properties of bulk metal materials (including yield stress, strain-hardening exponent (n) and strain-hardening rate (K)), which couples an experimental load-displacement curve with finite element method. The load–displacement curve was obtained from continuous instrumented indentation test. Then a hybrid particle swarm optimization was employed to minimize the deviation between experimental and simulated load-displacement curves. As a combination of particle swarm optimization and simulated annealing, the simulated annealing particle swarm optimization is an economical and effective algorithm to identify plastic parameters. It was observed that the maximum error of strain-hardening rate extracted from the macro indentation test was 8.2 percent contrast to that determined by the conventional tensile test, and the maximum error of strain-hardening exponent was 4.7% respectively.

A novel hybrid particle swarm optimization variant is proposed, which combines particle swarm optimization with Gaussian mutation operation based on random strategy. It applies Gaussian mutation on the positions of some randomly selected particles to enhance the search accuracy and convergence speed of swarm. The proposed algorithm can retain the diversity of population and improve the ability of global search. A suite of benchmark test functions is employed to evaluate the performance of the proposed method. The results have been compared with three state-of-the-art particle swarm optimization variants. Experimental results show that the Gaussian mutation and the random select strategy help the proposed algorithm to achieve faster convergence rate and provide better solutions in most of the problems. Further, the new algorithm has been tested on the high-dimensional problems. The results show that the proposed algorithm is not sensitive to high-dimensional problems and can even give a better performance. Moreover, the sensitivity analysis of the parameters was carried out and the setting of the parameters was given.

A novel hybrid particle swarm optimization (HPSO) is proposed, which the gradient descent learning algorithm is combined with modified particle swarm optimization (MPSO). Firstly, the MPSO was determined by linearly decreasing inertia weight and constriction factor weight to speed up global search, also crossover and mutation operation was embedded to avoid the common defect of premature convergence. Furthermore, gradient descent learning algorithm searched for the model parameter of radical basis function neural networks (RBFNN) to speed up the local search. Using the proposed HPSO algorithm based on RBFNN, we simulated the chaotic time series prediction of Henon map to test the validity. Simulation results show that the HPSO can accurately predict chaotic time series. lt provides an attractive approach to study the properties of complex nonlinear dynamic system

A new hybrid optimization algorithm is proposed for the problem of finding the minimum makespan in the job-shop scheduling environment. The new algorithm is based on the principle of particle swarm optimization (PSO). PSO employs a collaborative population-based search, which combines local search (by self experience) and global search (by neighboring experience), possessing high search efficiency. Simulated annealing (SA) employs certain probability to avoid becoming trapped in a local optimum. By reasonably combining these two different search algorithms, we develop a general, fast and easily implemented hybrid optimization algorithm, named HPSO. The effectiveness and efficiency of the new algorithm are demonstrated by comparing results with other algorithms on some benchmark problems. Comparing results indicate that PSO-based algorithm is a viable and effective approach for the job-shop scheduling problem.

Beetle Antennae Search (BAS) is an intelligent algorithm that mimics the beetle preying process. In BAS, there is a beetle which has a left and a right antennae sensing the intensity of the odor emitted by the food. The beetle learns the different sense of two antennas to determine the flight and finally finds the food. The BAS algorithm has been proved to have good optimizing speed and precision when it is applied for low-dimensional optimization problems. However, when solving high-dimensional problems, the algorithm is easily trapped into local optimum. In order to improve the optimization ability of BAS, we have combined particle swarm optimization (PSO) algorithm and proposed a new hybrid BAS and PSO algorithm (BAS-PSO). In BAS-PSO, firstly, the standard PSO is used for particle velocity and position updating. Particles learn ontology information and group optimal information for evolution. Then each particle in the swarm is taken as a beetle independently applying a local search with BAS algorithm. To verify the performance of BAS-PSO, four benchmark functions have been selected in our simulation experiments. The results show that the performance of BAS-PSO is better than BAS and PSO.

A hybrid intelligent algorithm by combining particle swarm optimization with chaos searching technique for solving nonlinear bilevel programming problems

Surface shape design in fluid flow problems via hybrid optimization algorithms

An attitude estimation approach based on the hybrid particle swarm optimization (HPSO) and radar cross section (RCS) precise prediction for the three-axis stabilized satellites in space is presented in this paper. The modified strategies with quantum behavior operator and cloud mutation based on the multi-phase particle swarm optimization are used to increase the diversity of particles, reduce the objective function evaluation times and improve the global searching ability. The simulated RCS sequences of three-axis stabilized satellites at the specified epoch are determined by the method combined the precise orbit and the RCS of satellites calculated by fast multipole method. The mean square error between measured RCS sequences and simulated RCS sequences is considered as the objective function. The attitude estimation problem is transferred to an optimization problem by minimizing the objective function with attitude angles, which is solved by HPSO. The operation procedure is shown clearly, and the set of measurement is analyzed. The numerical results of optimizing the test functions showing better behavior of the proposed algorithm method than standard particle swarm optimization in terms of accuracy are provided. Representative examples validate the accuracy and the anti-noise jamming ability of the proposed algorithm for attitude estimation of three-axis stabilized satellites.

This paper presents a method for parameter estimation of composite load model using a hybrid searching algorithm, which is performed by the combination of particle swarm optimization (PSO) and complex method. The method intends to enhance the ability of local searching around the solutions, also taking the advantage of global searching by PSO. The load model of interest, in this paper, is composed of a static model and the third order induction motor model, for power system stability analysis. The origin of the hybrid method is to further apply the complex method as a local search for better solutions, with the selected particles from the PSO procedure performed at PSO iteration.

Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of non-deterministic polynomial-time hard (NP-hard). Particle swarm optimization (PSO) has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE) algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO), is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self-adaptive DE (RBSADE), is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.

Methods to quantification of precursor damage in carbon fiber reinforced polymer (CFRP) composite structures are reported herein. These techniques include coda wave interferometry (CWI) and nonlinear ultrasonics (NLU). Since low-frequency Lamb wave propagation is insensitive to the early-stage material degradation, it is shown that decoding the information in coda wave can overcome this well-known limitation. To conclude this possibility, CWI technique is cross verified with a traditional high-frequency ultrasound method. To achieve this goal, a tensile–tensile fatigue experiment was designed for CFRP composite specimens. By inducing controlled fatigue damage in these structures, material states are assessed using low-frequency (<500 kHz) ultrasonic guided wave and high-frequency (>10 MHz) P-wave. Stretching guided coda wave is utilized to quantify the precursor damage as a unique approach in this article. However, such method could be illuded by the changes in the signals due to bonds and contacts. To verify if the CWI is successful, and to evaluate the precursor damage in composite structures, additional nonlinear analysis of ultrasonic signals from both guided waves and P-waves is performed. Higher order nonlinearities in both low-frequency guided wave and high-frequency P-wave propagation demonstrate the growth of precursor damage in CFRP composite structures. So does the CWI of low-frequency guided wave data. Accuracy of these ultrasonic techniques is validated with experimentally obtained remaining strengths of the fatigue specimens. With this verification it is envisioned that both CWI and NLU together could quantify the precursor damage in composite structures.

A novel strategy for optimal scheduling of short-term electric power generation of cascaded hydroelectric plants based on particle swarm optimization (PSO) and chance-constrained programming is presented to maximize the expected profit at a given risk level in this paper. Based on chance-constrained programming, in which some specified probability are given to simulate some uncertainties, such as water inflows, electricity prices, unit status, and so on. This paper proposes a model for short-term scheduling optimization of cascaded hydro plants, which includes uncertainties, spatial-temporal constraints among cascaded reservoirs, etc. A hybrid particle swarm optimization (HPSO), which is embedded with evolutionary algorithms, is presented to use for the solution of global optimization problems. Catastrophe theory, which is concerned with natural evolutionary or survival-of-the-fittest, is utilized as an indication of the premature converge of PSO, and the positions of particles are further adjusted in the search space according to chaos optimization. In this way, each particle competes and cooperates with its neighbors. The proof shows that HPSO is guaranteed to converge to the global optimization solution with probability one. The model presented is solved by a combination method of HPSO and Monte Carlo simulation. Finally, a numerical example is served for demonstrating the feasibility of the method developed.