Integrated the Simplified Interpolation and Clonal Selection into the Particle Swarm Optimization for Optimization Problems

Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

To improve the simulation accuracy of disease prediction model, a modified hybrid algorithm combining BP neural network (BPNN) with particle swarm optimization (PSO) algorithm based on chaos theory optimization is proposed considering BPNN is easy to fall into the local extremum. The chaos theory is used to optimize PSO algorithm to overcome the premature convergence of the traditional PSO algorithm. Then, the improved CAPSO algorithm is used to train the BPNN, to make full use of the global search characteristic of PSO algorithm and the local search ability of BPNN. The fitness function of the PSO algorithm is used as the energy function, and the optimization method of the improved hybrid algorithm is selected according to the specified number of evolution. An optimized network model was used to predict the prevalence of coronary heart disease. Compared with other algorithms such as BP neural network, the results show that the proposed algorithm has high accuracy and can significantly improve the quality of prediction.

Particle swarm optimisation (PSO) was developed by Kennedy and Eberhart in 1995 (Kennedy & Eberhart, 1995) inspired by the collective behaviour of natural birds or fish. PSO is a stochastic optimisation technique that uses a behaviour of population composed by many search points called particle. In spite of easy implementation in computer algorithms, it is well known as a powerful numerical optimizer. In the typical PSO algorithms, a set of particles searches the optimal solution in the problem space efficiently, by sharing the common attractor called global best. There are many modified versions of PSO by improving convergence property to a certain problem. While, a standard PSO is defined by Bratton and Kennedy (Bratton & Kennedy, 2007) to give a real standard for PSO studies. PSO seems as one of the evolutionary computations (ECs), and it has been shown that PSO is comparable to a genetic algorithm (Angeline, 1998). Thus, a lot of studies have demonstrated the effectiveness of PSO family in optimizing various continuous and discrete optimization problems. And a plenty of applications of PSO, such as the neural network training, PID controller tuning, electric system optimisation have been studied and achieved well results (Kennedy, 1997). However, PSO is often failed in searching the global optimal solution in the case of the objective function has a large number of dimensions. The reason of this phenomenon is not only existence of the local optimal solutions, the velocities of the particles sometimes lapsed into the degeneracy, so that the successive range is restricted in the sub-plain of the whole search hyper-plain. The sub-plane that is defined by finite number of particle velocities is a partial space in the whole search space. The issue of local optima in PSO has been studied and proposed several modifications on the basic particle driven equation (Parsopoulos et al., 2001; Hendtlass, 2005; Liang et al., 2006). There used a kind of adaptation technique or randomized method (e.g. mutation in evolutionary computations) to keep particles velocities or to accelerate them. Although such improvements work well and have ability to avoid fall in the local optima, the problem of early convergence by the degeneracy of some dimensions is still remaining, even if there are no local optima. Hence the PSO algorithm does not always work well for the high-dimensional function.

The particle swarm optimization (PSO) method was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully to various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. Some experimental results show that PSO has greater "global search" ability, but the "local search" ability around the optimum is not very good. This paper analyses the PSO method and presents the improved method, which is PSO with division of work (PSOwDOW). In order to enhance the 'local search" ability of PSO we divide the particle swarm into three sub swarms and each sub swarm has a different job in PSOwDOW. Experimental results show that PSOwDOW can overcome the deficiencies in the traditional PSO and reinforce the optimizing ability of the particle swarm.

MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

Recently, the use of the particle swarm optimization (PSO) technique for the reconstruction of microwave images has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, the basic PSO algorithm is easily trapping into local minimum and may lead to the premature convergence. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes di-cult. To overcome the premature convergence of PSO, we propose a new hybrid algorithm of particle swarm optimization (PSO), simulated annealing (SA) and tabu search algorithm (TS) for solving the scattering inverse problem. The incorporation of tabu search (TS) and simulated annealing (SA) as local improvement approaches enable the hybrid algorithm to overleap local optima and intensify its search ability in local regions. Reconstructions of dielectric scatterers from experimental inverse- scattering data are flnally presented to demonstrate the accuracy and e-ciency of the hybrid technique.

Considering the characteristics of the inconspicuous difference between targets and backgrounds and the low contrast in infrared images, an adaptive clustering algorithm based on fuzzy particle swarm optimization is used in the infrared image processing. Fuzzy C-mean (FCM) clustering algorithm is a local search algorithm because it is easily trapped local optimum and is sensitive to initial value effectively. On the other hand, particle swarm optimization (PSO) algorithm is a global optimization algorithm. By incorporating the local search ability of FCM algorithm and the global optimization ability of PSO and taking the clustering criterion function of FCM as the object function of PSO, a new hybrid image clustering algorithm based on particle swarm optimization and fuzzy C-mean algorithm is proposed. Experiments show that the new algorithm can get the optimal threshold by the maximum entropy.

Attraction towards Particle Swarm Optimization (PSO) algorithm has grown rapidly in the recent times due to being a simple and efficient optimization technique able to solve many continuous multimodal and multidimensional problems. In this paper, we have attempted to realize the PSO algorithm in Xilinx™ Vertex V FPGA (Field Programmable Gate Array). Standard benchmark functions such as Sphere, Rastrigin and Rosenbrock are considered to investigate the efficiency of the implementation. We have studied the effect of diverse swarm size to evaluate the performance of the PSO algorithm. IEEE 754 double precision floating point format is used to implement arithmetic modules. The results obtained in this work are compared with those reported in literature. Synthesis and simulation results demonstrate that FPGA implementation proposed in this paper converges faster compared to earlier reported work.

Nature Inspired Optimization Algorithms have become popular for solving complex Optimization problems. Two most popular Global Optimization Algorithms are Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Of the two, PSO is very simple and many Research Scientists have used PSO to solve complex Optimization Problems. Hence PSO is chosen in this work. The primary focus of this paper is on imitating God who created the nature. Hence the term "Artificial God Optimization (AGO)" is coined in this paper. AGO is a new field which is invented in this work. A new Algorithm titled "God Particle Swarm Optimization (GoPSO)" is created and applied on various benchmark functions. The World's first Hybrid PSO Algorithm based on Artificial Gods is created in this work. GoPSO is a hybrid Algorithm which comes under AGO Field as well as PSO Field. Results obtained by PSO are compared with created GoPSO algorithm. A list of opportunities that are available in AGO field for Artificial Intelligence field experts are shown in this work.

A model of wavelet neural network (WNN) using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of Particle Swarm Optimization (PSO) and gradient descent algorithm (GD), and is thus called HGDPSO. The Particle Swarm Optimizer has previously been used to train neural networks and generally met with success. The advantage of the PSO over many of the other optimization algorithms is its relative simplicity and quick convergence. But those particles collapse so quickly that it exits a potentially dangerous property: stagnation, which state would make it impossible to arrive at the global optimum, even a local optimum. HGDPSO was proposed for neural network training to avoid premature and eliminate stagnation in PSO. The effectiveness of the HGDPSO based WNN is demonstrated through the classification of the fault signals in rotating machinery. The simulated results show its feasibility and validity.

A hybrid algorithm of particle swarm optimization (PSO) and local search (LS) is proposed to solve the redundancy allocation problem for series-parallel multi-state systems. The proposed hybrid algorithm is able to design the system structure with a minimum cost to provide a desired level of availability. Unlike most of the previous studies that only consider homogeneous redundancy, the proposed algorithm allows for the heterogeneous redundancy technique. The universal generating function method is applied to evaluate system availability. The standard PSO is modified and novel LS strategies are integrated to adapt to the redundancy allocation problem. Case studies that facilitate comparisons between the proposed hybrid algorithm and other non-hybrid heuristics as well as meta-heuristics reported in the literature (such as genetic algorithms, tabu search, and ant colony optimization) are provided. The results illustrate the advantages of the proposed hybrid algorithm in terms of the solution quality or algorithm efficiency.

Hybrid particle swarm optimization (PSO) is such a kind of algorithms, which combine other algorithms or technologies to enhance the basic PSO. This paper compares three representative hybrid PSOs: breeding PSO, immune PSO, and chaos PSO. These comparisons are carried out in following aspects: the goals for hybrid, basic hybrid modes, the key implementation steps, optimization performances and so on. Through these comparisons and analysis, three hybrid algorithms' construction, implementation and respective application scope are summarized, especially, the optimization performances of three hybrid PSOs have been researched carefully. The goal is to find features of the three HPSOs and provide some guidence to develop new kinds of HPSO.

Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

The periodic inventory routing problem (PIRP) determines the delivery routing and the inventory policies for retailers from a supplier in a periodic time based on the minimal cost criterion. Since it is a non-deterministic polynomial-time (NP)-hard problem, a heuristic method is needed for this problem. In the past, different global heuristic methods, such as tabu search (TS) and simulated annealing (SA), have been proposed; however, they seem ineffective. Particle swarm optimization (PSO) is known as resolving multidimensional combinatorial problems such as PIRP; however, it is easily trapped in local optimality. The authors of this paper propose a hybrid heuristic method for the PIRP. The hybrid method integrates a large neighborhood search (LNS) into PSO to overcome the drawbacks of PSO and LNS. The PSO is adopted first. A local search is applied to each particle in different iterations. Then, a local optimal solution (particle) for each particle is obtained. Last, the LNS is applied to the global best solution to avoid becoming trapped in local optimality. The results show that the proposed hybrid heuristic method is 10.93 % better than the existing method and 1.86 % better than the pure heuristic method in terms of average cost.