The set-based particle swarm optimisation algorithm is a swarm-based meta-heuristic that has gained popularity in recent years. In contrast to the original particle swarm optimisation algorithm, the set-based particle swarm optimisation algorithm is...
Particle Swarm Optimization (PSO) is a numerical optimization technique based on the motion of virtual particles within a multidimensional space. The particles explore the space in an attempt to find minima or maxima to the optimization problem. The...
Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting cont...
As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisf...
As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstr...
In this paper, we propose a particle swarm optimization variant based on a novel evaluation of diversity (PSO-ED). By a novel encoding of the sub-space of the search space and the hash table technique, the diversity of the swarm can be evaluated effi...
Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been s...
In software engineering, errors or faults in software systems often lead to critical social problems. One effective methodology to tackle this problem is model checking, which is an automated formal verification technique. In traditional model checki...
In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The propo...
This paper proposes an improved dual-center particle swarm optimization (IDCPSO) algorithm which can effectively improve some inherent defects of particle swarm optimization algorithms such as being prone to premature convergence and low optimization...
Particle swarm optimization (PSO) is a population-based optimization technique that has been applied extensively to a wide range of engineering problems. This paper proposes a variation of the original PSO algorithm for unconstrained optimization, du...
Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with mul...
Evolutionary computation algorithms (EC) and swarm intelligence have been widely used to solve global optimization problems. The optimal solution for an optimization problem is called by different terms in EC and swarm intelligence. It is called indi...
Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found...
In this article, we present a new algorithm called Particle Swarm Contour Search (PSCS)—a Particle Swarm Optimisation inspired algorithm to find object contours in 2D environments. Currently, most contour-finding algorithms are based on image p...
Portfolio optimization is a multi-objective optimization problem (MOOP) with risk and profit, or some form of the two, as competing objectives. Single-objective portfolio optimization requires a trade-off coefficient to be specified in order to balan...
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor...
Particle Swarm Optimization is a metaheuristic optimization algorithm widely used across a broad range of applications. The algorithm has certain primary advantages such as its ease of implementation, high convergence accuracy, and fast convergence s...
Particle swarm optimization (PSO) as a swarm intelligence-based optimization algorithm has been widely applied to solve various real-world optimization problems. However, traditional PSO algorithms encounter issues such as premature convergence and a...
This paper addresses the problem of tracking a high-speed ballistic target in real time. Particle swarm optimization (PSO) can be a solution to overcome the motion of the ballistic target and the nonlinearity of the measurement model. However, in gen...
Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress i...
Although particle swarm optimization (PSO) has been successfully applied to solve optimization problems, its optimization performance still encounters challenges when dealing with complicated optimization problems, especially those with many interact...
Artificial intelligence techniques, such as particle swarm optimization, are used to solve problems throughout society. Optimization, in particular, seeks to identify the best possible decision within a search space. Problematically, particle swarm o...
This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust result...
The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and...
The majority of complex research problems can be formulated as optimization problems. Particle Swarm Optimization (PSO) algorithm is very effective in solving optimization problems because of its robustness, simplicity, and global search capabilities...
In order to overcome the several shortcomings of Particle Swarm Optimization (PSO) e.g., premature convergence, low accuracy and poor global searching ability, a novel Simple Particle Swarm Optimization based on Random weight and Confidence term (SPS...
Brain storm optimization (BSO) and particle swarm optimization (PSO) are two popular nature-inspired optimization algorithms, with BSO being the more recently developed one. It has been observed that BSO has an advantage over PSO regarding exploratio...
Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems that cannot be solved using traditi...
The emergence of autonomous vehicles offers the potential to eliminate traditional traffic lanes, enabling vehicles to navigate freely in two-dimensional spaces. Unlike conventional traffic constrained by physical lanes, autonomous vehicles rely on r...
The partitioned step particle swarm optimization (PSPSO) introduces a two-fold searching mechanism that increases the search capability of Particle Swarm Optimization. The first layer involves the γ and λ, values which are introduced to describe the...
In hybrid mixed-flow workshop scheduling, there are problems such as mass production, mass manufacturing, mass assembly and mass synthesis of products. In order to solve these problems, combined with the Spark platform, a hybrid particle swarm algori...
A Mobile ad hoc Network (MANET) is a collection of wireless mobile nodes that temporarily establish a network without centralized administration or fixed infrastructure. Designing the routing of adequate routing protocols is a major challenge given t...
Statistical population reconstruction models based on maximum likelihood and minimum chi-square objective functions provide a robust and versatile approach to estimating the demographic dynamics of harvested populations of wildlife. These models empl...
An improved adaptive particle swarm algorithm with guiding strategy (GSAPSO) was proposed, and it was applied to solve the reactive power optimization (RPO). Four kinds of particles containing the main particles, double central particles, cooperative...
Particle swarm optimization (PSO) is a bio-inspired stochastic optimization algorithm that simulates the foraging behavior of birds. Despite its simplicity and efficiency, PSO often suffers from premature convergence and a poor balance between explor...
Large-scale particle swarm optimization (PSO) has long been a hot topic due to the following reasons: Swarm diversity preservation is still challenging for current PSO variants for large-scale optimization problems, resulting in difficulties for PSO...
Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optim...
This paper presents a particle swarm tracking algorithm with improved inertia weight based on color features. The weighted color histogram is used as the target feature to reduce the contribution of target edge pixels in the target feature, which mak...
Swarms and assemblies are ubiquitous in nature and they can perform complex collective behaviors and cooperative functions that they cannot accomplish individually. In response to light, some colloidal particles (CPs), including light active and pass...
Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominat...
The Particle Swarm Optimisation (PSO) algorithm was inspired by the social and biological behaviour of bird flocks searching for food sources. In this nature-based algorithm, individuals are referred to as particles and fly through the search space s...
The method of searching for an optimal solution inspired by nature is referred to as particle swarm optimization. Differential evolution is a simple but effective EA for global optimization since it has demonstrated strong convergence qualities and i...
The location of a logistics center is very important in a logistics system, as the success of the location determines the whole logistics system’s structure, shape, and mode, and not only affects the logistics center’s own operating costs...
The optimization of injection–production parameters is an important step in the design of gas injection development schemes, but there are many influencing factors and they are difficult to determine. To solve this problem, this paper optimizes...
This study presents a reliable particle swarm optimizer for sizing optimization of truss structures. This population-based stochastic optimization approach is based on the principle that each particle communicates its position and function value to a...
Since the power grid grows and the necessity for higher system efficiency is due to the increasing number of renewable energy penetrations, power system operators need a fast and efficient method of operating the power system. One of the main problem...
Optimisation-based methods are enormously used in the field of data classification. Particle Swarm Optimization (PSO) is a metaheuristic algorithm based on swarm intelligence, widely used to solve global optimisation problems throughout the real worl...
Microgrids have been widely used due to their advantages, such as flexibility and cleanliness. This study adopts the hierarchical control method for microgrids containing multiple energy sources, i.e., photovoltaic (PV), wind, diesel, and storage, an...
High-dimensional optimization problems are ubiquitous in every field nowadays, which seriously challenge the optimization ability of existing optimizers. To solve this kind of optimization problems effectively, this paper proposes an elite-directed p...
of 129