A Dynamic Reconstruction Bare Bones Particle Swarm Optimization Algorithm

The bare bones particle swarm optimization (BBPSO) algorithm has been proved to be an effective tool for single-objective optimization problems over continuous search spaces. In this paper, a fission-fusion hybrid bare bones particle swarm optimizer (FHBBPSO) is proposed. The FHBBPSO combines a fission strategy and a fusion strategy to sample new positions of the particles. The fission strategy aims at splitting the search space. Particles are assigned to different local groups to sample the corresponding regions. On the other side, the fusion strategy aims at narrowing the search space. Marginal groups will be gradually merged by the central groups until there is only one group left. The two strategies work together for the theoretical optimal value. To confirm the searching ability of the FHBBPSO, the proposed method runs over the IEEE Congress on Evolutionary Computation 2014 (CEC2014) benchmark functions. Also, several famous evolutionary methods are used in the control group. The experimental results and analysis suggest that the FHBBPSO is a highly competitive optimization algorithm for single-objective functions.

A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

Multi swarm bare bones particle swarm optimization with distribution adaption

Particle swarm optimisation PSO is a population-based stochastic search algorithm, which simulates the social behaviour of bird flocking or fish schooling. Many previous studies have shown that PSO is an effective optimisation technique in evolutionary optimisation community. However, the standard PSO still suffers from premature convergence when solving complex multimodal problems. In this paper, we propose a new PSO algorithm called multi-strategy based bare bones PSO MPSO. The MPSO introduces generalised opposition-based learning GOBL and two neighbourhood search strategies into the original bare bones PSO. Simulation study is conducted on 13 well-known benchmark functions. The results show that MPSO achieves better results than the standard PSO and two other PSO algorithms.

Sparse Code Multiple Access (SCMA) is a class of competitive non-orthogonal multiple access (NOMA) that is proposed to support massive Machine Type of Communication (m-MTC). In this paper, we propose a new scheme to construct the SCMA codebooks, where we jointly optimize the rotation angles and mother codebook parameters. The basic idea is to make the upper bound of the error probability as smaller as possible. To realize it, we search for suitable angles and parameters through the bare bones particle swarm optimization (BBPSO) algorithm. The simulation results demonstrate that compared with the original SCMA, Star-QAM SCMA and LDS the new proposed SCMA codebooks provides better performance in both additive white Gaussian noise (AWGN) and Rayleigh channels, moreover, it also gives better performance in high overloading.

Particle swarm optimization (PSO) is a population-based evolutionary computation technique. The standard parallel Particle swarm optimization (PSO) is a promising scheme for solving NP-complete problems due to its fast convergence, fewer parameter settings and ability to fit dynamic environmental characteristics. A modified version of standard parallel particle swarm optimization algorithm named Agent-based Parallel PSO (PPSO) is presented to solve the large-scale optimization problem at a faster convergence rate. The new algorithm provides the information exchange among the particle sets which aims to accelerate the rate of convergence. Parallelization is based on the client-server model in which the particle set with global best value acts as a server and others are clients with agents in a single iteration. The agents contain the worst value information of respective particle set which they have to exchange with the global best value among particle sets in the swarm. The server is the center of data exchange, which deals with agents and manages the sharing of global best position among the individual clients by the replacement of worst position with global best position. The information exchange in particle sets has been done so that the new result will be the better as compared to previous result.

Reservoir optimal scheduling is a typical multi-constrained,dynamic,non-linear optimization problem.To solve this problem,a Dynamic Programming-Particle Swarm Optimization (DP-PSO) algorithm was used to solving.This algorithm used the optimal Dynamic Programming (DP) principle to convert the reservoir optimal scheduling problem to multistage decision-making sub-problems;the solution of each sub-problem was got by particle swarm optimization algorithm.The numerical experiments show that with more time in calculation,the reliability of the DP-PSO is superior to the general DP algorithm,and the calculation time of DP-PSO is less than DP-Genetic Algorithm (DP-GA).

As the demand for mobile services grows exponentially, the focus on wireless network optimization has been changed from Quality of Service (QoS) for the network to Quality of Experience (QoE) for users. The network optimization research about QoS in the past cannot surely meet the requirements of the users' QoE. Therefore, in this paper, a handover solution is proposed to improve the QoE while considering the QoE balance for a LTE network that provides different services. Compared with other optimization of QoE nowadays, the proposed solution balances and improves the QoE of users. The proposed solution controls handover parameter by running a handover optimization algorithm based on the dynamic particle swarm optimization (DPSO) in a central controller, which finally optimizes the overall QoE and reduces the proportion of users with extremely poor QoE. The simulation results show that the DPSO algorithm ensures the quality of the solution and increases the speed of convergence by nearly twice that of the standard particle swarm optimization algorithm (SPSO). After adopting the DPSO handover solution, the overall QoE of users is increased by 6.22 % and 4.59 % while the variance of users' QoE is decreased by 14.2 % and 22.6 %, compared with the full handover solution and the traditional handover solution respectively. The number of users with QoE less than 1.9 is reduced to 0 with the proposed solution.

Training neural networks is a complex task with great importance in the field of supervised learning. In the training process, a set of input?output patterns is repeated to an artificial neural network (ANN). From those patterns weights of all the interconnections between neurons are adjusted until the specified input yields the desired output. In this paper, a new hybrid algorithm is proposed for global optimization of connection weights in an ANN. Dynamic swarms are shown to converge rapidly during the initial stages of a global search, but around the global optimum, the search process becomes very slow. In contrast, the gradient descent method can achieve faster convergence speed around the global optimum, and at the same time, the convergence accuracy can be relatively high. Therefore, the proposed hybrid algorithm combines the dynamic particle swarm optimization (DPSO) algorithm with the backpropagation (BP) algorithm, also referred to as the DPSO-BP algorithm, to train the weights of an ANN. In this paper, we intend to show the superiority (time performance and quality of solution) of the proposed hybrid algorithm (DPSO-BP) over other more standard algorithms in neural network training. The algorithms are compared using two different datasets, and the results are simulated.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

This article presents a novel parallel multi-swarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard single-swarm PSOs for global optimization of very large-scale multimodal functions. Different from the existing multi-swarm structures, the multiple swarms work in parallel, and the search space is partitioned evenly and dynamically assigned in a weighted manner via the roulette wheel selection (RWS) mechanism. This parallel, distributed framework of the PMSO algorithm is developed based on a master–slave paradigm, which is implemented on a cluster of PCs using message passing interface (MPI) for information interchange among swarms. The PMSO algorithm handles multiple swarms simultaneously and each swarm performs PSO operations of its own independently. In particular, one swarm is designated for global search and the others are for local search. The first part of the experimental comparison is made among the PMSO, standard PSO, and two state-of-the-art algorithms (CTSS and CLPSO) in terms of various un-rotated and rotated benchmark functions taken from the literature. In the second part, the proposed multi-swarm algorithm is tested on large-scale multimodal benchmark functions up to 300 dimensions. The results of the PMSO algorithm show great promise in solving high-dimensional problems.

Biometric models are typically designed a priori using limited number of samples acquired from complex environments that change in time during operations. Therefore, these models are often poor representatives of the biometric trait to be recognized. To circumvent this problem, ensemble of classifiers can be used to integrate solutions obtained from multiple diverse classifiers. In this paper, two dynamic particle swarm optimization (DPSO) algorithms are compared for the evolution of classifier ensembles during supervised incremental learning of newly-acquired data samples in video-based face recognition. Using the properties of these population-based optimization algorithms, an incremental DPSO learning strategy for adaptive classification systems (ACSs) is employed to evolve a pool of fuzzy ARTMAP classifiers while an heterogeneous ensemble is selected through a greedy search process that seeks to maximize both performance and diversity. The performance of dynamic niching PSO (DNPSO) and speciation PSO (SPSO) algorithms is assessed in terms of classification rate, resource requirements and diversity for different incremental learning scenarios of new data blocks extracted from real-world video streams. Simulation results indicate that both DPSO algorithms can efficiently create accurate ensembles while reducing computational complexity. In addition, directly selecting representative subswarm particles to form diversified classifier ensembles significantly reduces the computational complexity.

Many problems in the real-world have more than one objective, with at least two objectives in conflict with one another. In addition, at least one objective changes over time. These kinds of problems are called dynamic multi-objective optimisation problems (DMOOPs). Studies have shown that both the quantum particle swarm optimisation (QPSO) and charged particle swarm optimisation (CPSO) algorithms perform well in dynamic environments, since they maintain swarm diversity. Therefore, this paper investigates the effect of using either QPSOs or CPSOs in the sub-swarms of the dynamic vector-evaluated particle swarm optimisation (DVEPSO) algorithm. These DVEPSO variations are then compared against the default DVEPSO algorithm that uses gbest PSOs and DVEPSO using heterogeneous PSOs that contain both charged and quantum particles. Furthermore, all of the aforementioned DVEPSO configurations are compared against the dynamic multi-objective optimisation (DMOPSO) algorithm that was the winning algorithm of a comprehensive comparative study of dynamic multi-objective optimisation algorithms. The results indicate that charged and quantum particles improve the performance of DVEPSO, especially for DMOOPs with a deceptive POF and DMOOPs with a non-linear POS.

Decision letter for "An overlapping peaks separation algorithm for ion mobility spectrometry based on second‐order differentiation and dynamic inertia weight particle swarm optimization algorithm"

This paper investigates a novel inventory and distribution planning model with non-conforming items disposal (NIDPNCID) under fuzzy random environment to minimize the whole process cost. In this process, a certain fraction or a random number of produced items are defective. These non-conforming items are rejected in order to improve the consumer satisfaction. To solve the problem, a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm with fuzzy random simulation is proposed, which can be easy to implement. In more specific terms, DP-based PSO can reduce the dimensions of a particle by using the state equation, which significantly reduced the solution space.