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1 – 10 of over 2000The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem…
Abstract
Purpose
The purpose of this paper is to develop an effective and efficient approach to exploit meta‐heuristic in particle swarm optimization (PSO) for the job shop scheduling problem (JSP), a class of NP‐hard optimization problems. The approach is to be built on a PSO with multiple independent swarms. PSO was inspired by bird flocking and animal social behaviors. The particles operate collectively like a swarm that flies through the hyperdimensional space to search for possible optimal solutions. The behavior of the particles is influenced by their tendency to learn from their personal past experience and from the success of their peers to adjust their flying speed and direction. Research in fusing the multiple‐swarm concept into PSO is well‐established in solving single objective optimization problems and multimodal problems.
Design/methodology/approach
This study examines the optimization of the JSP via a search space division scheme and use of the meta‐heuristic method of PSO by assigning each machine in a JSP an independent swarm of particles. The use of multiple swarms in PSO is motivated by the idea of “divide and conquer” to reduce the computational complexity incurred through solving a NP‐hard combinatorial optimization problem. The resulted design, JSP/PSO algorithm, fully exploits the computing power presented by the multiple‐swarm PSO.
Findings
Simulation experiments show that the proposed JSP/PSO algorithm can effectively solve the JSP problems from small to median size. If certain mechanism of information sharing between swarms can be incorporated, it is believed that the new design could offer even more computing power to tackle the large‐sized problems.
Originality/value
The proposed JSP/PSO algorithm is effective in solving JSPs. The proposed algorithm shows considerable promise when searching the space of non‐delay schedules. It demands relatively lower number of function evaluations compared to other state‐of‐the‐art. The drawback to the JSP/PSO is that the GT scheduling adopted is too computationally expensive. Future works will address this concern.
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Stefan Janson, Daniel Merkle and Martin Middendorf
The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are…
Abstract
Purpose
The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub‐swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub‐swarms PSO can be used for example in applications where more than one optimum has to be found.
Design/methodology/approach
In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub‐swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network.
Findings
Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale‐free network and ring like networks.
Originality/value
The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self‐organization and have no central control.
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Danilo Ferreira de Carvalho and Carmelo José Albanez Bastos‐Filho
Particle swarm optimization (PSO) has been used to solve many different types of optimization problems. In spite of this, the original version of PSO is not capable to find…
Abstract
Purpose
Particle swarm optimization (PSO) has been used to solve many different types of optimization problems. In spite of this, the original version of PSO is not capable to find reasonable solutions for some types of problems. Therefore, novel approaches to deal with more sophisticated problems are required. Many variations of the basic PSO form have been explored, targeting the velocity update equation. Other approaches attempt to change the communication topology inside the swarm. The purpose of this paper is to propose a topology based on the concept of clans.
Design/methodology/approach
First of all, this paper presents a detailed description of its proposal. After that, it shows a graphical convergence analysis for the Rosenbrock benchmark function. In the sequence, a convergence analysis for clan PSO with different parameters is performed. A comparison with star, ring, focal, von Neumann and four clusters topologies is also performed.
Findings
The paper's simulation results have shown that the proposal obtained better results than the other topologies for the benchmark functions selected for this paper.
Originality/value
The proposed topology for PSO based on clans provides a novel form for information distribution inside the swarm. In this approach, the topology is determined dynamically during the search process, according to the success rate inside each clan.
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Qingxia Li, Xiaohua Zeng and Wenhong Wei
Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective…
Abstract
Purpose
Multi-objective is a complex problem that appears in real life while these objectives are conflicting. The swarm intelligence algorithm is often used to solve such multi-objective problems. Due to its strong search ability and convergence ability, particle swarm optimization algorithm is proposed, and the multi-objective particle swarm optimization algorithm is used to solve multi-objective optimization problems. However, the particles of particle swarm optimization algorithm are easy to fall into local optimization because of their fast convergence. Uneven distribution and poor diversity are the two key drawbacks of the Pareto front of multi-objective particle swarm optimization algorithm. Therefore, this paper aims to propose an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.
Design/methodology/approach
In this paper, the proposed algorithm uses adaptive Cauchy mutation and improved crowding distance to perturb the particles in the population in a dynamic way in order to help the particles trapped in the local optimization jump out of it which improves the convergence performance consequently.
Findings
In order to solve the problems of uneven distribution and poor diversity in the Pareto front of multi-objective particle swarm optimization algorithm, this paper uses adaptive Cauchy mutation and improved crowding distance to help the particles trapped in the local optimization jump out of the local optimization. Experimental results show that the proposed algorithm has obvious advantages in convergence performance for nine benchmark functions compared with other multi-objective optimization algorithms.
Originality/value
In order to help the particles trapped in the local optimization jump out of the local optimization which improves the convergence performance consequently, this paper proposes an improved multi-objective particle swarm optimization algorithm using adaptive Cauchy mutation and improved crowding distance.
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Zijing Ye, Huan Li and Wenhong Wei
Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such…
Abstract
Purpose
Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.
Design/methodology/approach
Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.
Findings
Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.
Originality/value
Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.
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Hui-Zhong Xiong, Xin Yang, Yong-Nan He and Yong Huang
This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the…
Abstract
Purpose
This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy method.
Design/methodology/approach
This paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element method.
Findings
The study’s findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length growth.
Originality/value
The improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.
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This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great…
Abstract
Purpose
This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique.
Design/methodology/approach
In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs.
Findings
Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively.
Research limitations/implications
In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only.
Practical implications
To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m2 network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s.
Practical implications
This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption.
Originality/value
PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.
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The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems.
Abstract
Purpose
The purpose of this paper is to improve the efficiency of particle optimization method by using direct and indirect surrogate modeling in inverse design problems.
Design/methodology/approach
The new algorithm emphasizes the use of a direct and an indirect design prediction based on local surrogate models in particle swarm optimization (PSO) algorithm. Local response surface approximations are constructed by using radial basis neural networks. The principal role of surrogate models is to answer the question of which individuals should be placed into the next swarm. Therefore, the main purpose of surrogate models is to predict new design points instead of estimating the objective function values. To demonstrate its merits, the new approach and six comparative algorithms were applied to two different test cases including surface fitting of a geographical terrain and an inverse design of a wing, the averaged best-individual fitness values of the algorithms were recorded for a fair comparison.
Findings
The new algorithm provides more than 60 per cent reduction in the required generations as compared with comparative algorithms.
Research limitations/implications
The comparative study was carried out only for two different test cases. It is possible to extend test cases for different problems.
Practical implications
The proposed algorithm can be applied to different inverse design problems.
Originality/value
The study presents extra ordinary application of double surrogate modeling usage in PSO for inverse design problems.
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Shafiullah Khan, Shiyou Yang and Obaid Ur Rehman
The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem.
Abstract
Purpose
The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem.
Design/methodology/approach
A modified PSO algorithm is designed.
Findings
The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm.
Originality/value
Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.
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Yanxia Liu, Zhikai Hu and JianJun Fang
The three-axis magnetic sensors are mostly calibrated by scalar method such as ellipsoid fitting and so on, but these methods cannot completely determine the 12 parameters of the…
Abstract
Purpose
The three-axis magnetic sensors are mostly calibrated by scalar method such as ellipsoid fitting and so on, but these methods cannot completely determine the 12 parameters of the error model. A two-stage calibration method based on particle swarm optimization (TSC-PSO) is proposed, which makes full use of the amplitude invariance and direction invariance of Earth’s magnetic field vector.
Design/methodology/approach
The TSC-PSO designs two-stage fitness function. Stage 1: design a fitness function of the particle swarm by the amplitude invariance of the Earth’s magnetic field to obtain a preliminary error matrix G and the bias error B. Stage 2: further design the fitness function of the particle swarm by the invariance of the Earth’s magnetic field to obtain a rotation matrix R, thereby determining the error matrix uniquely.
Findings
The proposed TSC-PSO can completely determine 12 unknown parameters in error model and further decrease the maximum fluctuation error of the Earth’s magnetic field amplitude and the absolute error of heading.
Practical implications
The proposed TSC-PSO provides an effective solution for three-axis magnetic sensor error compensation, which can greatly reduce the price of magnetic sensors and be used in the fields of Earth’s magnetic survey, drilling and Earth’s magnetic integrated navigation.
Originality/value
The proposed TSC-PSO has significantly improved the magnetic field amplitude and heading accuracy and does not require additional heading reference. In addition, the method is insensitive to noise and initialization conditions, has good robustness and can converge to a global optimum.
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