A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization

A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method’s best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima because the particles could quickly get closer to the best particle. At such situations, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by adding a Cauchy mutation on the best particle so that the mutated best particle could lead all the rest of particles to the better positions. Experimental results on many well-known benchmark optimization problems have shown that HPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization

Allocation for earthquake emergency shelters is a complicated geographic optimization problem because it involves multiple sites, strict constraints, and discrete feasible domain. Huge solution space makes the problem computationally intractable. Traditional brute-force methods can obtain exact optimal solutions. However, it is not sophisticated enough to solve the complex optimization problem with reasonable time especially in high-dimensional solution space. Artificial intelligent algorithms hold the promise of improving the effectiveness of location search. This article proposes a modified particle swarm optimization (PSO) algorithm to deal with the allocation problem of earthquake emergency shelter. A new discrete PSO and the feasibility-based rule are incorporated according to the discrete solution space and strict constraints. In addition, for enhancing search capability, simulated annealing (SA) algorithm is employed to escape from local optima. The modified algorithm has been applied to the allocation of earthquake emergency shelters in the Zhuguang Block of Guangzhou City, China. The experiments have shown that the algorithm can identify the number and locations of emergency shelters. The modified PSO algorithm shows a better performance than other hybrid algorithms presented in the article, and is an effective approach for the allocation problem of earthquake emergency shelters.

A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems

Particle swarm optimization (PSO) is a new promising evolutionary algorithm for the optimization and search problem. One problem of PSO is its tendency to trap into local optima due to its mechanism in information sharing. This paper proposes a novel hybrid PSO, namely (HPSO) technique by merging both a mutation operator and natural selection to solve the problem of premature convergence. By introducing Cauchy mutation and evolutionary selection strategy based on roulette wheel selection, HPSO could greatly reduce the probability of trapping into local optimum. HPSO is proposed to improve the performance of fragile watermarking based DCT which results in enhancing both the quality of the watermarked image and the extracted watermark. After embedding watermark to the original image in the frequency domain, the conversion of real numbers of the modified coefficients in frequency domain to integer numbers in spatial domain produces some rounding errors problem. This problem results in completely different of the extracted watermark from the embedded watermark. The new developed PSO with evolutionary operators is carried out for correcting the rounding errors by training a translation map used to modify the inverse DCT (IDCT) coefficients from real to integer numbers. The experimental results show the superiority of the proposed algorithm comparing with the standard PSO for improving the performance of DCT fragile watermarking. Besides, it has been shown that the developed PSO is faster in convergence and the obtained results proved to have higher fitness than the other algorithm.

This paper studies the two-stage assembly flow shop problem (TAFSP) considering aging effects of the machines and preventive maintenance activities. At the first stage, m-1 parallel machines process parts of each jobs, and at the second stage, related parts of the jobs are assembled by one assembly machine. As the machines work on the jobs, their tools get aged. Aging effects on the machines causes that they will not be able to complete the jobs in the same time could as they were new or when they are operating jobs immediately after their preventive maintenance activity. Processing times of the job are related to the positions, in which it is located after the last preventive maintenance. The job that is operated in a position immediately after the preventive maintenance activity on a machine has its standard processing time. However, the processing time of the jobs operated in the further positions increase based on the number of the positions. The machines return to the initial condition after each preventive maintenance activity. The objective is to schedule the jobs on the machines and determine when the preventive maintenance activities get done on them in order to minimize the total weighted tardiness and maintenance costs. An integer mathematical model is presented for the problem and its validation is shown by solving an example in small scale. Since two-stage assembly flow shop problem is NP-hard, in order to solve the problem in medium and large scale two meta-heuristic algorithms, hybrid genetic algorithm (HGA) and hybrid particle swarm optimization (HPSO) are proposed. These algorithms are the hybrid version of genetic algorithm and particle swarm optimization representatively with simulated annealing. The algorithms are tuned by using Taguchi method, and are used to solve many numerical examples. Finally, the statistical analysis illustrates that the performance of HPSO is better than HGA.

Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of non-deterministic polynomial-time hard (NP-hard). Particle swarm optimization (PSO) has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE) algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO), is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self-adaptive DE (RBSADE), is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.

Instrumented indentation test is a promising non-destructive method to determine mechanical properties. This paper proposes a new approach to determine the plastic properties of bulk metal materials (including yield stress, strain-hardening exponent (n) and strain-hardening rate (K)), which couples an experimental load-displacement curve with finite element method. The load–displacement curve was obtained from continuous instrumented indentation test. Then a hybrid particle swarm optimization was employed to minimize the deviation between experimental and simulated load-displacement curves. As a combination of particle swarm optimization and simulated annealing, the simulated annealing particle swarm optimization is an economical and effective algorithm to identify plastic parameters. It was observed that the maximum error of strain-hardening rate extracted from the macro indentation test was 8.2 percent contrast to that determined by the conventional tensile test, and the maximum error of strain-hardening exponent was 4.7% respectively.

Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm

Hybrid PSO and GA approach for optimizing surveyed asphalt pavement inspection units in massive network

A hybrid particle swarm optimization and hill climbing algorithm for task scheduling in the cloud environments

Novel Artificial Human Optimization Field Algorithms - The Beginning

Fuzzy programming offers a powerful means of handling optimization problems with fuzzy parameters. Fuzzy programming has been used in different ways in the past. The particle swarm optimization (PSO) has been applied successfully to continuous nonlinear constrained optimization problems, neural network, etc. But we have not been found to use PSO for fuzzy programming in literature. In this paper, we combined with fuzzy simulation, neural network and PSO to produce a hybrid intelligent algorithm. Based on this hybrid intelligent algorithm, we introduced for solving fuzzy expected value models. Some numerical examples are given to illustrate the algorithm is effective and powerful.

Experimental study of a new hybrid PSO with mutation for economic dispatch with non-smooth cost function