Two-layer Particle Swarm Optimization Research Articles

An optimized demand-response operation method of regional integrated energy system considering 5G base station energy storage

The scheduling technology of regional integrated energy system is one of the key technologies to realize carbon neutralization by utilizing wind-power. Aiming at the optimal scheduling problem of regional electrothermal integrated energy system considering wind-power utilization and load side energy consumption, this paper proposes an optimized demand-response operation method of regional integrated energy system considering 5G base station energy storage. The regional integrated energy system of load side demand response is constructed based on the comprehensive consideration of technical and economic factors such as wind-power utilization and economic costs and load side peak valley difference. Finally, a two-layer particle swarm optimization method is proposed to solve the model. The experimental results show that the proposed method can effectively achieve wind-power utilization, economic dispatch and reduce the peak valley difference through load side demand response, which can improve the economic efficiency, environmental protection and low-carbon operation of regional integrated energy system.

Particle swarm optimizer with crossover operation

A particle swarm optimization algorithm with crossover operation (PSOCO) is proposed. In the proposed PSOCO, two different crossover operations are employed in order to breed promising exemplars. By performing crossover on the personal historical best position of each particle, the effective guiding exemplars are constructed and they maintain a good diversity. In turn, these high quality exemplars are used to guide the evolution of particles. PSOCO is two-layer particle swarm optimization with positive feedback mechanism. In order to test the performance of PSOCO, we use a set of widely used benchmark functions. The experimental results demonstrate that the proposed PSOCO is a competitive optimizer in terms of both solution quality and efficiency.

Fragmented protein sequence alignment using two-layer particle swarm optimization (FTLPSO)

This paper presents a Fragmented protein sequence alignment using two-layer PSO (FTLPSO) method to overcome the drawbacks of particle swarm optimization (PSO) and improve its performance in solving multiple sequence alignment (MSA) problem. The standard PSO suffers from the trapping in local optima, and its disability to do better alignment for longer sequences. To overcome these problems, a fragmentation technique is first introduced to divide the longer datasets to a number of fragments. Then a two-layer PSO algorithm is applied to align each fragment, which has ability to deal with unconstrained optimization problems and increase diversity of particles. The proposed method is tested on some Balibase benchmarks of different lengths. The numerical results are compared with CLUSTAL Omega, CLUSTAL W2, TCOFFEE, KALIGN, and DIALIGN-PFAM. It has been shown that better alignment scores have been achieved using the proposed technique FTLPSO. Further, studies on PSO update equation’s parameters and the parameters of the used scoring functions are presented and discussed.

An adaptive two-layer particle swarm optimization with elitist learning strategy

This study presents an adaptive two-layer particle swarm optimization algorithm with elitist learning strategy (ATLPSO-ELS), which has better search capability than classical particle swarm optimization. In ATLPSO-ELS, we perform evolution on both the current swarm and the memory swarm, motivated by the tendency of the latter swarm to distribute around the problem’s optima. To achieve better control of exploration/exploitation searches in both current and memory swarms, we propose two adaptive division of labor modules to self-adaptively divide the swarms into exploration and exploitation sections. In addition, based on the orthogonal experimental design and stochastic perturbation techniques, an elitist learning strategy module is introduced in the proposed algorithm to enhance the search efficiency of swarms and to mitigate premature convergence. A comprehensive experimental study is conducted on a set of benchmark functions. Compared with various state-of-the-art PSOs and metaheuristic search variants, ATLPSO-ELS performs more competitively in the majority of the benchmark functions.

Two-layer particle swarm optimization with intelligent division of labor

Early studies in particle swarm optimization (PSO) algorithm reveal that the social and cognitive components of swarm, i.e. memory swarm, tend to distribute around the problem's optima. Motivated by these findings, we propose a two-layer PSO with intelligent division of labor (TLPSO-IDL) that aims to improve the search capabilities of PSO through the evolution memory swarm. The evolution in TLPSO-IDL is performed sequentially on both the current swarm and the memory swarm. A new learning mechanism is proposed in the former to enhance the swarm's exploration capability, whilst an intelligent division of labor (IDL) module is developed in the latter to adaptively divide the swarm into the exploration and exploitation sections. The proposed TLPSO-IDOL algorithm is thoroughly compared with nine well-establish PSO variants on 16 unimodal and multimodal benchmark problems with or without rotation property. Simulation results indicate that the searching capabilities and the convergence speed of TLPSO-IDL are superior to the state-of-art PSO variants.

Two-layer particle swarm optimization for unconstrained optimization problems

In this article, a two-layer particle swarm optimization (TLPSO) is proposed to increase the diversity of the particles so that the drawback of trapping in a local optimum is avoided. In order to design the TLPSO, a structure with two layers (top layer and bottom layer) is proposed so that M swarms of particles and one swarm of particles are generated in the bottom layer and the top layer, respectively. Each global best position in each swarm of the bottom layer is set to be the position of the particle in the swarm of the top layer. Therefore, the global best position in the swarm of the top layer influences indirectly the particles of each swarm in the bottom layer so that the diversity of the particles increases to avoid trapping into a local optimum. Besides, a mutation operation is added into the particles of each swarm in the bottom layer so that the particles leap the local optimum to find the global optimum. Finally, some optimization problems of different types of high dimensional functions are used to illustrate the efficiency of the proposed method.