Multi-objective Optimization of FCC Separation System Based on Particle Swarm Optimization

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.

Because the sensors are constrained in energy capabilities, low-energy clustering has become a challenging problem in high-density wireless sensor networks (HDWSNs). Usually, sensor nodes tend to be tiny devices along with constrained clustering abilities. To have a low communication energy consumption, a low-energy clustering scheme should be designed properly. In this work, a new cloned chaotic parallel evolution algorithm (CCPEA) is proposed, and a low-energy clustering model is established to lower the communication energy consumption of HDWSNs. By introducing CCPEA into the low-energy clustering, an objective function is designed for evaluating the communication energy consumption. For this problem, we define a clone operator to minimize the communication energy consumption of HDWSNs, use the chaotic operator to randomly generate the initial population to expand the search range to avoid local optimization, and find the parallel operator to speed up the convergence speed. In the experiment, the effect of CCPEA is compared to heuristic approaches of particle swarm optimization (PSO) and simulated annealing (SA) for the HDWSNs with different numbers of sensors. Simulation experiments demonstrate that the presented CCPEA method achieves a lower communication energy consumption and faster convergence speed than PSO and SA.

Prediction of coal spontaneous combustion temperature based on improved grey wolf optimizer algorithm and support vector regression

The method of Particle Swarm Optimization (PSO) is an epitome in optimization process for solving classification, clustering, and many other multi-dimensional problems. PSO is found to be stronger than few state of the art population-based coup such as Ant colony optimization (ACO), genetic algorithms (GAs), Optimization of the herd of elephants known as Elephant herd optimization (EHO) together with other algorithms under certain real-life scenarios like path finding, feature selection, etc. Originally PSOs were implemented by means of optimizing the velocity/speed and position update equations so that it could locate the best global position of the particles. Many researchers have designed different approaches by combining human behavioral aspects to these equations in order to further optimize the PSOs performance. In this text we have proposed the use of a 4-staged teacher-learning behavior (TLB) inspired PSO for optimizing the system performance. Under different test functions, the proposed algorithm was tested and evaluated and its performance was compared relative with the modern state of the art PSO systems. It is observed that the proposed PSO reduces the convergence time by more than 20%, and also reduces the overall computational complexity to half. We conclude this text by making some acute observations about the implemented system, and recommend methods which can be used for further optimizing the system 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.

In this paper, a clustering algorithm based on improved particle swarm optimization algorithm is proposed by using the characteristics of particle swarm optimization algorithm with strong global search and optimization ability. PSO algorithm is used for global searching, and K-global optimal clustering centers are used as the initial clustering centers of K-means algorithm,. Then the final clustering results are obtained by running k-means algorithm. According to the fitness variance of particle swarm optimization, the algorithm determines the switching time between the former part of PSO and the latter part of K-means algorithm, and it deduces the cluster head considering energy and distance. The simulation results show that the algorithm balances the energy consumption more effectively than LEACH and significantly prolongs the network's lifetime.

The phase diversity (PD) algorithm is an image-based co-phasing error detection method for segmented mirror synthetic aperture optical systems. Particle swarm optimization (PSO) is suitable for PD for its fast convergence speed and simple structure. However, with the increase of the numbers of subapertures, optimization of cost function formed by the PD algorithm becomes a high-dimension non-linear optimization problem, which would lead PSO to result in a premature solution. Regarding the problem above, this paper presented a modified PSO in the PD algorithm to co-phase the segmented primary mirror, which overcame drawbacks of the traditional PSO, such as premature convergence and deactivation of particles, and enhanced the dig ability of the algorithm. Vast simulation results show that the modified PSO in the PD algorithm can effectively restore the segmented primary mirror, reducing the peak-to-valley (PV) value to 0.0012λ and the root mean square error to 0.0007λ, and raising the Strehl ratio to over 0.999. A comparison was also conducted to show that the modified PSO has advantages over two existing algorithms in higher accuracy and faster convergence speed.

Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In particular, the discipline focuses on the behavior of social insects such as fish schools and bird flocks and colonies of ants, termites, bees, and wasps. The most well-known examples of systems studied by swarm intelligence are particle swarm optimization (PSO) and ant colony optimization (ACO). Particle swarm optimization mimics the behavior of fish schooling and bird flocking. PSO is a population-based stochastic optimization strategy with fast convergent speed than general evolutionary algorithms (EAs). Different from EAs, each particle employs not only position information but also the velocity information. They communicate good positions to each other and adjust their own positions according to their decision. In PSO, a number of simple entities—the particles—are placed in the search space of some problem or function, and each evaluates the objective function at its current location. Each particle then determines its movement through the search space by combining some aspect of the history of its own current and best (best-fitness) locations with those of one or more members of the swarm, with some random perturbations. The next iteration takes place after all particles have been moved. Eventually, the swarm as a whole, like a flock of birds collectively foraging for food, moves close to an optimum of the fitness function. Ant colony optimization is a probabilistic technique for solving computational hard problems which can be reduced to finding optimal paths. The main idea is the indirect communication between the ants by means of chemical pheromone trails. The special issue on the swarm intelligence is dedicated to the latest work in the theory and applications in this exciting area. Our aim is to provide a useful reference for understanding new trends on swarm intelligence. After a detailed review process, a total of four papers were selected to reflect the call thematic vision. The contents of these studies are briefly described as follows. The first paper entitled ‘Analysis of emergent symmetry breaking in collective decision making’ by Heiko Hamann, Thomas Schmickl, Heinz Worn, and Karl Crailsheim investigates a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent– agent encounters and is inspired by the aggregation behavior of honey bees. In this article, symmetry breaking is defined, several symmetry-breaking measures are proposed, and phenomenon of emergent symmetry breaking within authors’ observed system is reported. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed, and two macroscopic features are determined that significantly influence the symmetry-breaking behavior. In addition, the connection between the ability of the Z. Cui (&) Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, 030024 Shanxi, Taiyuan, China e-mail: cuizhihua@gmail.com

Based on the deficiency of the traditional stochastic resonance method in multi-parameters optimization and the difficulty of adaptive stochastic resonance with genetic algorithm in multiple line-spectrums detection, a new method of adaptive swept stochastic resonance based on particle swarm optimization (PSO) is proposed. The signal-noise-ratio (SNR) of outputs of bistable system is determined as the fitness function of PSO and the line-spectrum can be detected by the synchronous optimization of multi-parameters. Simultaneously, the adaptive swept stochastic resonance, combined with twice sampling algorithm and PSO algorithm, achieves the detection of multiple line-spectrums. The simulation results show that the proposed method can detect multiple line- spectrums signal effectively, and has advantages of simplicity and fast convergence speed.

This article presents the implementation and comparison of fruit fly optimization (FOA), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms in solving the mobile robot path planning problem. FOA is one of the newest nature-inspired algorithms while PSO and ACO has been in existence for a long time. PSO has been shown by other studies to have long search time while ACO have fast convergence speed. Therefore there is need to benchmark FOA performance with these older nature-inspired algorithms. The objective is to find an optimal path in an obstacle free static environment from a start point to the goal point using the aforementioned techniques. The performance of these algorithms was measured using three criteria: average path length, average computational time and average convergence speed. The results show that the fruit fly algorithm produced shorter path length (19.5128 m) with faster convergence speed (3149.217 m/secs) than the older swarm intelligence algorithms. The computational time of the algorithms were in close range, with ant colony optimization having the minimum (0.000576 secs).
 Keywords: Swarm intelligence, Fruit Fly algorithm, Ant Colony Optimization, Particle Swarm Optimization, optimal path, mobile robot.

Uniform magnetic fields are widely used in atomic magnetometers, MRI and other fields. The traditional method of solving closed-form equations is usually used to design highly uniform magnetic field coils. In this paper, a new intelligent algorithm, wolf pack algorithm (WPA), is proposed to design the coil structure of a highly uniform magnetic field. This method can replace the traditional method to obtain the coil parameters. We additionally use a discrete optimization method to solve the rounding error. According to the investigation, compared with particle swarm optimization (PSO) algorithm, WPA has stronger global optimization ability, faster convergence speed and multiple optimization strategies. We set appropriate coil constraints (structural and process constraints) and simplify the multi-dimensional solutions to one-dimensional solutions to reduce the difficulty of operation. The distribution of magnetic field intensity of three axes of coils is analyzed by the finite element analysis method, and the 1%, 0.1%, and 0.01% uniform regions are studied. Under the same conditions, the uniform magnetic field regions of the coils designed by the PSO and WPA, as well as a single pair of axial and radial coils are compared. The performance results of multiple pairs of coils are superior to those of a single pair of coils. In general, the uniformity of the axial and radial magnetic field coils designed by the WPA is better than that designed by the PSO. Finally, the experimental value of the magnetic field uniformity is nearly consistent with the theoretical value.

PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability of AIS. In IPSO, training data is presented to the algorithm one by one, and the training proceed is a one-shot incremental algorithm. Besides, the swarm does not converge to a single solution; instead, each particle is a part of the classifier, and the whole memory population is taken as the integral classifier to the problem. Compared the results of standard PSO and IPSO in several benchmark problems from the UCI data sets, we found that IPSO achieved a better classification accuracy than standard PSO in most cases. It is also competitive with some of the algorithms most commonly used for classification.

ABSTRACTThe optimization of chaotic systems remains a challenge because the search space of the design parameters can have several orders of magnitude, causing that the corresponding eigenvalues can be very sparse, thus producing unnecessary long simulation times. This imposes the need of estimating the step‐size of the numerical method that discretizes the ordinary differential equations. In this manner, the proposed work shows the optimization of chaotic systems, by applying differential evolution (DE), grey wolf optimization (GWO), and particle swarm optimization (PSO) algorithms. Within the optimization loop, is estimated taking into account the inverse of the highest eigenvalue, and the total time simulation is estimated by taking the inverse of the lowest eigenvalue. The constraints consider that a chaotic system is simulated only if there exist two complex eigenvalues and if the Fourier transform of the chaotic time series has a spectrum area in a certain threshold. A single‐objective function is associated to maximize the Kaplan–Yorke dimension , and then PSO, DE, and GWO are executed with the same number of runs, generations, and population individuals. Their performances are compared by Wilcoxon and Levene tests. The best solutions obtained for each optimization algorithm and for each chaotic system are used to implement pseudo‐random number generators (PRNG). Finally, the PRNGs that passed NIST and TestU01 tests are implemented into a field‐programmable gate array.

Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.

Testing is an important way to ensure and improve the quality of software system. However, it is a time-consuming and labor-intensive activity. In the paper, our main concern is software structural testing, and propose a search-based test data generation solution. In our framework, particle swarm optimization (PSO) technique is adopted due to its simplicity and fast convergence speed. For test data generation problem, the inputs of program under test are encoded into particles. Once a set of test inputs is produced, coverage information can be collected by test driver. Meanwhile, the fitness value of branch coverage can be calculated based on such information. Then, the fitness is used for PSO to adjust the search direction. Finally, the test data set with the highest coverage rate is yielded. In addition, eight well-known programs are used for experimental analysis. The results show that PSO-based approach has a distinct advantage compared to the traditional evolutionary algorithms such as generic algorithm and simulated annealing, and also outperforms comprehensive learning-PSO both in covering effect and in evolution speed.