Multirobot task allocation based on an improved particle swarm optimization approach

In this paper, an improved particle swarm optimization (IPSO) method, which is based on standard particle swarm optimization (PSO) and a changing range genetic algorithm (GA), is proposed, and it is applied in the form-finding analysis of a suspension bridge installation. The new method has been integrated into the successive iteration method of the form-finding analysis, and it transfers the usual iterative process of form-finding analysis into a single objective optimization problem. The computation formulation and computer code of the IPSO have been developed and implemented, and benchmark examples are studied to test the performance of the IPSO method through numerical experiments. The results show that IPSO can be used as a viable solution methodology for the form-finding analysis in various initial, boundary, and complicated load conditions to overcome the limitations of the conventional Newton-Raphson (N-R) iterative method. Furthermore, compared with GA, PSO, and GA-PSO, IPSO has greater accuracy and efficiency. Finally, the method is applied in an actual form-finding analysis of a multitower suspension bridge (Yingwuzhou Yangtze River Bridge) to validate the practicality and feasibility of the IPSO-based method in engineering design. The case study indicates that IPSO provides superior convergent solutions for the analysis considered here when compared to the N-R method, and the computation time is affordable for large-scale engineering bridge construction design and analysis.

Towards locating time-varying indoor particle sources: Development of two multi-robot olfaction methods based on whale optimization algorithm

In order to overcome the drawbacks of falling into local extremum and lower optimization precision of standard particle swarm optimization (PSO) algorithm, multipopulation strategy, adaptive dynamic adjustment strategy and co-evolution mode are introduced into the standard PSO algorithm in order to propose an improved coevolutionary PSO(MPACEPSO) algorithm based on multi-strategy evolution mode and multi-population co-evolutionary mechanism. In the evolutionary process of MPACEPSO algorithm, the multi-population strategy is used to divide the population into several subpopulations, which use different co-evolutionary model to evolve. These sub-populations are influenced and promoted each other in order to realize the exchange of information and co-evolution among the sub-populations, improve the convergence speed and search precision of MPACEPSO algorithm, and effectively suppress the appearance of the local optimum. The adaptive dynamic adjustment strategy of inertia weight is used to keep the diversity of population, reduce the probability of falling into the local extremum. Finally, the ZDT functions are selected to test the optimization performance of proposed MPACEPSO algorithm. The experimental results show that the proposed MPACEPSO algorithm has faster convergence speed, stronger global search ability, higher solving precision and better dynamic optimization performance. The experimental result analysis shows that the proposed MPACEPSO algorithm is insensitive to parameters and easy to be used in solving the complex optimization problems.

This paper presents an improved particle swarm optimization (PSO) method and applies it to the design of a hybrid asymmetric load modulated balanced amplifier (H-ALMBA) with high linearity. A description is provided of the H-ALMBA topology as well as the improved PSO method with individually self-adaptive hyperparameters (Improved PSO-ISH). The design process as well as the optimization procedure are described in detail. In addition to output power and efficiency, linearity performance, including AM/AM and AM/PM characteristics, is also considered during optimization. The performance of H-ALMBA optimized by the proposed method is presented and compared to the initial design, the ADS built-in optimizer, and other methods of PSO that are currently available. Due to the introduction of Latin hypercube sampling (LHS) and a secondary optimization step in the improved PSO-ISH method, the optimization performance has been greatly enhanced. According to the measured results, the H-ALMBA designed using the proposed method achieves a saturation drain efficiency (DE) between 59.5% and 64.8%, and a DE of 45.7%-58.1% in the 10dB output back-off state (OBO), with an output power of 41.5 dBm-44.2 dBm from 1.8GHz to 2.6GHz. The variation range of AM/AM is less than 4.1dB and that of AM/PM is less than 11.5°, demonstrating high linearity. Additionally, modulated signal tests are conducted. A 20-MHz 5G new radio (NR) signal with 10-dB peak-to-average power ratios (PAPRs) is used. A high level of linearity is achieved without the use of digital predistortion (DPD).

An improved particle swarm optimization method with multistage inertia weights is proposed for the design of gradient coils for use in magnetocardiography systems. With this method, the design of the gradient coils is transformed into a constrained nonlinear objective optimization problem, and the particle swarm optimization method with multistage inertia weights is used to solve this problem and thus obtain optimized size parameters for the coils. Furthermore, through comprehensive use of several parameters in combination, including the magnetic field derivative, the target field information, and the optimization algorithm, a highly linear magnetic field is produced by considering the constraints of the coil structure and the turns ratio. Simulation results show that the linearity spatial deviation of the designed gradient coil is one order of magnitude lower than that of the Maxwell coil with a sphere of a radius of $0.5~R$ . Using the proposed method, two optimized gradient coils are manufactured for magnetocardiography system applications with magnetic fields that are matched well with those produced by the theoretical simulation model. Experimental results show that the maximum of the linearity spatial deviation of the proposed gradient coils reaches $2.2\times 10^{-3}$ and $1.4\times 10^{-3}$ along the $z$ -axis within ± $0.5~R$ , respectively.

This paper presents the solution of optimal power flow (OPF) using particle swarm optimization (PSO).The objective function is minimize power loss by adjustment the power control variables and at the same time satisfying the equality and inequality constraints.The proposed particle swarm optimization (PSO) method is compared with newton's Raphson method (conventional method) approach on the standard IEEE 14 bus system.The analysis indicated that Particle Swarm Optimization (PSO) method was the most efficient method in terms of minimizing the power loss.This can be concluded that the Artificial Intelligence (AI) method such as Particle Swarm Optimization (PSO) is the most suitable and efficient method for analysing the Optimal Power Flow (OPF) problem in terms of minimizing power loss.

In order to reduce power losses in a power system and to improve voltage profile, researchers have examined and studied optimizing Photovoltaic Power distributed generation (DG) which is location and size in a power system, but the results have some drawbacks. Several approaches developed to make this important issue more efficient, including coming up with new algorithms and improving those already in existence. Many of the proposed algorithms are only concerned with the real power loss, however. Voltage stability control is a critical factor in modern power systems, which makes incorporating reactive power losses in optimizing DG allocation for voltage profile improvement necessary. The goal of this work is to solve this issue by combining Genetic Algorithm and Improved Particle Swarm optimization to optimize DG size and location by considering both real and reactive power losses. Power loss sensitivity factors and real and reactive power flow factors used in identifying which buses will receive DGs. A MATLAB-based program was developed and tested on a test system using distributed generators, considering the proposed method. As compared to Genetic Algorithm, Particle Swarm optimization and Improved Particle Swarm optimization methods, the Hybrid Genetic Algorithm Improved Particle Swarm optimization method is better for reducing both real and reactive power losses.

The reconstruction algorithm has a hot research in compressed sensing. Matching pursuit algorithm has a huge computational task, when particle swarm optimization has been put forth to find the best atom, but it due to the easy convergence to local minima, so the paper proposed a algorithm ,which based on improved particle swarm optimization. The algorithm referred above combines K-mean and particle swarm optimization algorithm. The algorithm not only effectively prevents the premature convergence, but also improves the K-mean’s local. These findings indicated that the algorithm overcomes premature convergence of particle swarm optimization, and improves the quality of image reconstruction.

The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.

Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a ground-breaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.

Improved particle swarm optimization combined with chaos

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for the functions generated by MLPs and SRNs as compared to BP and PSO. These three algorithms are also trained on several benchmark functions to confirm results.

Lotka-Volterra competition model has been applied in life sciences including competition between species, predicting the Aeromonas hydrophila growth on fish surface. The competition model also has been used in social sciences, including competition in the Korean stock market and competition between two types of bank, namely commercial bank and rural bank in Indonesia. It is well-known that the analytical solution of the Lotka-Volterra is unknown. Here gradient-base methods such as Newton method and Levenberg-Marquardt are difficult to be implemented to estimate paramaters of the model. In order to estimate parameters in the model, one need to use heuristic method such as genetic algorithm, particle swarm optimization, firefly algorithm or other heuristic methods. In this paper, we compared performance of particle swarm optimization and firefly algorithm in parameter estimation of Lotka-Volterra type competition model. Here we used the profit data of commercial bank and rural bank, where the data cited from literature. We found the mean absolute percentage error (MAPE) of firefly algorithm is a little bit smaller than the error of particle swarm optimization method. We also found variance of the error of firefly algorithm is lower than the particle swarm optimization method. Hence, for parameter estimation of Lotka-Volterra competition model, firefly algorithm is more beneficial than the particle swarm optimization method.

In view of the characteristics of the preparation of an adhesive process which is lengthy, nonlinear, time-varying, big inertia and pure delay, A Proportional-Integral-Derivative (PID) algorithm is proposed based on the improved particle swarm optimization. Because there are drawbacks in the design of PID controller, an improved particle swarm optimization which takes into account a number of performances is propesed to modified parameters of PID controllers. In which the variation of genetic algorithm is introduced to the standard particle swarm optimization algorithm, which can avoid local maximum points, thus the preferable PID controller parameters are easily obtained. Applying the algorithm to the preparation of an adhesive process, the temperature of polymerizing-kettle can be tracked and controlled. Simulation and factual running shows that the algorithm is effective and has excellent performance.

Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical optimizer. In the typical PSO algorithms, a set of particles searches the optimal solution in the problem space efficiently, by sharing the common attractor called global best. There are many modified versions of PSO by improving convergence property to a certain problem. While, a standard PSO is defined by Bratton and Kennedy (Bratton & Kennedy, 2007) to give a real standard for PSO studies. PSO seems as one of the evolutionary computations (ECs), and it has been shown that PSO is comparable to a genetic algorithm (Angeline, 1998). Thus, a lot of studies have demonstrated the effectiveness of PSO family in optimizing various continuous and discrete optimization problems. And a plenty of applications of PSO, such as the neural network training, PID controller tuning, electric system optimisation have been studied and achieved well results (Kennedy, 1997). However, PSO is often failed in searching the global optimal solution in the case of the objective function has a large number of dimensions. The reason of this phenomenon is not only existence of the local optimal solutions, the velocities of the particles sometimes lapsed into the degeneracy, so that the successive range is restricted in the sub-plain of the whole search hyper-plain. The sub-plane that is defined by finite number of particle velocities is a partial space in the whole search space. The issue of local optima in PSO has been studied and proposed several modifications on the basic particle driven equation (Parsopoulos et al., 2001; Hendtlass, 2005; Liang et al., 2006). There used a kind of adaptation technique or randomized method (e.g. mutation in evolutionary computations) to keep particles velocities or to accelerate them. Although such improvements work well and have ability to avoid fall in the local optima, the problem of early convergence by the degeneracy of some dimensions is still remaining, even if there are no local optima. Hence the PSO algorithm does not always work well for the high-dimensional function.