An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization

Li, Dongyang; Guo, Weian; Lerch, Alexander; Li, Yongmei; Wang, Lei; Wu, Qidi

doi:10.1016/j.swevo.2020.100789

Cited by 68 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lan et al [64] design a two-phase learning swarm optimizer (TPLSO), which involves two learning strategies called "mass learning" and "elite learning", respectively. Li et al [65] Now, many new swarm intelligence algorithms have been proposed and also applied to LSOPs, such as the sine-cosine algorithm (SCA), whale optimization algorithm (WOA), and social spider algorithm. Li et al [70] improve SCA by using nonlinear random convergence parameter and dynamic inertia weight strategy (termed as DSCA).…”

Section: ) Novel Learning and Updating Strategies For Easmentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

Large-scale optimization problems (LSOPs) are challenging because the algorithm is difficult in balancing too many dimensions and in escaping from trapped bottleneck dimensions. To improve solutions, this paper introduces targeted modification to the certain values in the bottleneck dimensions. Analogous to gene targeting (GT) in biotechnology, we experiment on targeting the specific genes in candidate solution to improve its trait in differential evolution (DE). We propose a simple and efficient method, called GT-based DE (GTDE), to solve LSOPs. In the algorithm design, a simple GT-based modification is developed to perform on the best individual, comprising probabilistically targeting the location of bottleneck dimensions, constructing a homologous targeting vector, and inserting the targeting vector into the best individual. In this way, all the bottleneck dimensions of the best individual can be probabilistically targeted and modified to break the bottleneck and to provide global guidance for more optimal evolution. Note that the GT is only performed on the globally best individual and is just carried out as a simple operator that is added to the standard DE. Experimental studies compare the GTDE with some other state-of-the-art large-scale optimization algorithms, including the winners of CEC2010, CEC2012, CEC2013, and CEC2018 competitions on large-scale optimization. The results show that the GTDE is efficient and performs better than or at least comparable to the others in solving LSOPs.Manuscript received….revised….accepted….

show abstract

Section: ) Novel Learning and Updating Strategies For Easmentioning

confidence: 99%

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Wang

Jian

Zhan

et al. 2023

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

show abstract

“…Cheng et al [57] proposed a mutation operator based on the Alpha-stable distribution to enhance the swarm diversity and avoid premature convergence. Li et al [58] changed the particles' velocity update rule to decouple exploration and exploitation. Xue et al [59] used multiple velocity and position update rules which are chosen probabilistically whose parameters are adapted according to the effectiveness of each strategy.…”

Section: B Particle Swarm Optimizationmentioning

confidence: 99%

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Omidvar

Yao

2022

IEEE Trans. Evol. Computat.

View full text Add to dashboard Cite

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

show abstract

“…PSO [35] is a swarm intelligence optimization algorithm proposed in 1995 and its theoretical idea comes from the predation behavior of birds. This algorithm is highly efficient and easy to implement, which is suitable for the complex nonlinear optimization problems [36,37]. Furthermore, the layout optimization for subsea production control systems belongs to this problem.…”

Section: Adaptive Mutation Particle Swarm Algorithmmentioning

confidence: 99%

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

2021

View full text Add to dashboard Cite

The subsea production control system, characterized by a complex and diverse structure and high cost, is one of the essential parts of a subsea production system. The rational layout of the subsea production control system is essential to reduce development costs and ensure safe production in offshore fields. Most previous studies on layout design in offshore fields have focused on the oil- and gas-gathering system. However, the layout of the subsea production control system has not thoroughly been researched to date and the seabed terrain and integral optimization have rarely been discussed. This paper focuses on the multi-layer star structure and multi-layer star-tree structure, two common layout structures of subsea production control systems, and establishes the corresponding model with obstacle and seabed terrain conditions. Obtaining the lowest possible total cost was the aim of the model. A hybrid algorithm combining the adaptive mutation particle swarm algorithm and the A-star algorithm was applied to integrally optimize the subsea distribution unit and umbilical touch down point positions, the pipe connection topology and pipe routes. The practicality of this approach is demonstrated by designing a layout with one FPSO and 22 subsea control modules. The results indicate that the multi-layer star-tree layout structure has a lower total cost compared to that of the multi-layer star layout structure. In addition, the results were compared with a case that ignores the seabed terrain, indicating differences in the total construction cost. This method provides engineers with quantitative references and reliable cost estimates to make decisions regarding the layout of the subsea production control system.

show abstract

An adaptive particle swarm optimizer with decoupled exploration and exploitation for large scale optimization

Cited by 68 publications

References 46 publications

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

A Review of Population-Based Metaheuristics for Large-Scale Black-Box Global Optimization—Part II

Integral Layout Optimization of Subsea Production Control System Considering Three-Dimensional Space Constraint

Contact Info

Product

Resources

About