Artificial God Optimization – A Creation

In this paper, an improved approach integrating genetic algorithm and adaptive particle swarm optimization with feed forward neural networks for image compression is proposed. The hybrid genetic algorithm with a novel mutation strategy and particle swarm optimization is used to train the neural network to near global optimum weights and thresholds at first. Then the network is trained with gradient descending learning algorithm to obtain the optimal network parameters. Then, the trained network is applied to the image compression. Results show that at the same compression rate the application of optimized neural network in image compression will achieve better image quality compared with the application of traditional neural network.

The applications of the Internet of Things in different areas and the resources that demand these applications are on the increase. However, the limitations of the IoT devices such as processing capability, storage, and energy are challenging. Computational offloading is introduced to ameliorate the limitations of mobile devices. Offloading heavy data size to a remote node introduces the problem of additional delay due to transmission. Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. The proposed method uses a rank accuracy estimation model to decide the rank-1 value to be applied for the decomposition. Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. The proposed algorithm improved the response time, delay, number of offloaded tasks, throughput, and energy consumption of the IoT requests. The simulation is implemented with iFogSim and java programming language. The proposed method can be applied in smart cities, monitoring, health delivery, augmented reality, and gaming among others.

Multicast routing is an effective way to communicate among multiple hosts in a network. For multimedia applications, the routing algorithms should consider many Quality of Service (QoS) parameters such as delay, cost and so on to find a new route. However, to find routes with two or more QoS parameters is an NP-hard problem. This paper describes a new evolutionary scheme for optimization of multicast QoS routing based on the hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The upper-half of the best-performing individuals in a population are regarded as elites. Instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. This scheme can simultaneously optimize the cost of the tree, the maximum end-to-end delay, the average delay and the maximum link utilization. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. It has revealed an efficient method of the reconstruction of multicast tree topology and the experimental results demonstrated better performance than the conventional GA optimization method.

Peer-to-Peer technology is one of the most popular techniques nowadays, and it brings some security issues, so the recognition and management of P2P applications on the internet is becoming much more important. The selection of protocol attributes is significant to the problem of P2P identification. To overcome the shortcomings of current methods, a new P2P identification algorithm based on genetic algorithm and particle swarm optimization is proposed. The attributes of network traffic flows are selected and assigned the corresponding weightings according to their importance by evolutionary algorithms. The experimental results show that this algorithm can effectively select the subset from multiple attributes that can best reflect the differences among some most popular P2P protocols and also between P2P and non-P2P protocols. The identification rate is improved by the method of feature weighting calculated by particle swarm optimization. With this algorithm, the average identification rate of popular P2P protocols reaches to 96.3%.

A study on the development of a microwave tomography imaging algorithm for detecting the malignant tumor in the breast is presented. Tomography modality is based on the electromagnetic reflections generated by the dielectric contrast between breast tissue types at microwave frequencies. In the tomography method, finite difference time domain method (FDTD) has been used as a technique for calculating electromagnetic scattered fields. In this paper, we propose a novel hybrid optimization technique for solving the inverse scattering problem which uses the binary Genetic algorithm (BGA) and binary particle swarm optimization (BPSO). The convergence rate of this proposed algorithm is around 4 times better than the regular BGA. The proposed FDTD/hybrid BGA-BPSO method has the ability to reconstruct the heterogeneous and dispersive breast tissues to provide a quantitative image of permittivity and conductivity profile of the breast. The proposed technique is capable to detect the size, location and permittivity and conductivity of the tumor even though it is surrounded by benign and fibroglandular tissues.

The potential of renewable energy sources is massive as they can in principle meet many times the world's energy demand. In this paper, Solar PV parameter extraction is performed using Artificial Immune System (AIS). A novel objective function based on derivative of maximum power with respect to voltage for solar PV double diode model is proposed. For validation and performance evaluation, the results obtained using the proposed approach employing AIS, are compared with Genetic algorithm (GA) and particle swarm optimization (PSO). As compared to GA and PSO, AIS using the proposed approach has the least error. The results also showcase that the proposed approach with AIS outclasses GA and PSO in terms of absolute error for all the three PV models under various irradiation conditions.

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

— In this paper, two optimization algorithms i.e., Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) are combined to produce a hybrid population-based algorithm PSOGSA. The approach of HPSOGSA is to use the assessment of PSO using the social thinking capability and the manipulation of GSA using the local search proficiency. To increase the performance of HPSOGSA, some benchmark functions are used. Two standard database ORL and YALE is used to assess the classification performance of the proposed method. Classification accuracy is compared for diverse number of training samples per class. Further lead of proposed method is confirmed by analyzing percentage improvement in classification accuracy. With limited accessibility of training sample, percentage enhancement is very successful for face recognition applications. Keywords: Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (HPSOGSA). Cite this Article Purvaa Saxena, Gargi Mishra, Apoorva Aggarwal, Ayush Priyadarshi, Nitin Kumar, Rahul. Pattern Recognition Using Hybrid Meta-Heuristic. Journal of Artificial Intelligence Research & Advances. 2019; 6(2): 100–108p.

With the rapid development of China’s medical insurance industry, fraud irregularities are also constantly increasing with renovation of forms and means. Then the way of detecting the fraud automatically and efficiently has become the major issue on which much attention is paid. This paper selects genetic algorithm (GA) and particle swarm optimization (PSO) as the basic algorithm. Spontaneously, in order to comprehensively utilize the local search space of GA and swift convergence capability of PSO algorithm as well as improve the poor local search of PSO by introducing simulated annealing (SA), this paper proposes an algorithm that utilizes three classical algorithms to optimize support vector machine (GASAPSO-SVM) and ultimately verifies its algorithm on the medical insurance fraud dataset.

Scheduling problems, as one of the classic combinatorial optimization problems are essential issues in many fields. Various meta-heuristic algorithms have been adopted to solve scheduling problems. However, parameter control problem is still crucial to the performance of algorithms. In this paper, we propose a self-adaptive parameter control method based on entropy and security market line, fully considering the characteristics scheduling problems. It consists of two key parts: locus-entropy strategy and parameter-control strategy. Firstly, the entropy on each genetic locus is calculated to accurately evaluate the population status of scheduling algorithms. Then, a parameter-control strategy based on the conception of security market line is proposed to address the issue that the nature of multipeak in scheduling problems makes algorithms fall into local optimal solutions. The strategy maintains the solutions of good quality and eliminate the solutions of poor quality by using locus-entropy as feedback. Through our method, the balance between exploitation and exploration is kept in algorithms to perform well in scheduling problems with different dimensions and characteristics. These strategies are tightly linked to adjust parameters adaptively without introducing new parameters, so that meta-heuristic algorithms equipped with the proposed approach are able to find a better solution. Moreover, our parameter control method is universal for meta-heuristic algorithms. The proposed approach is hybrid with genetic algorithm and particle swarm optimization. The hybrid algorithms are first compared with the standard algorithms in multipeak benchmark functions, then with other variants of the standard algorithms in real-world single and multi-objective scheduling problems. The results demonstrate that the proposed approach is valid for different kinds of algorithms to enhance the performance of solving a variety of scheduling problems.

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.

The paper presents non-traditional solutions to the problem of selective harmonic elimination (SHE) in induction motor drives fed by three-phase voltage-source inverter (VSI). The problem is independently solved using genetic algorithms (GA) and particle swarm optimisation (PSO). The inverter switching angles are determined such that the low-order harmonics up to the 17th are eliminated, while the fundamental component is set at a desired value. Findings of the research show that both routines could converge to accurate solutions. PSO outperforms GA in terms of the time of convergence and obtained value of the objective function, while GA is characterised with better robustness. The performance of the VSI-fed induction motor drive is studied under the SHE switching pattern. Results clearly show the merits of applying the SHE technique to the induction motor drive.

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

The power amplifier (PA) is naturally nonlinear in its operation. To get good energy efficiency, the PA is needed to function at its saturation level and results in the generation of the nonlinear outputs. To counter the nonlinearization in PA, a pre-distorter is appropriately designed and introduced in front of the PA. In this paper, an innovative pre-distorter is introduced by employing adaptive genetic algorithm (AGA) and their results are compared with that of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The Wiener model is considered to model the PA, and the pre-distorter is built up by means of Hammerstein model. The new approach simulated using MATLAB and the outputs achieved are analyzed. The pre-distortion using AGA has produced better results in terms of MSE compared to that produced using PSO and GA optimization algorithms.

Evolutionary Algorithms are good optimizing agents that use the evolutionary concepts to evolve systems. The complex fitness landscapes are a problem that restricts the performance of these algorithms. Besides specifying the correct parameter is always very important. In this chapter we study the means by which we can execute a number of evolutionary algorithms in a variety of models to give an overall optimal performance to the entire network. We first discuss the Island Model Algorithm where the individuals are evolved in multiple islands with regular interaction. We then study a related algorithm called the Hierarchical Fair Competition algorithm. Here we would separate the various individuals into classes as per their fitness values. Each of these would have a separate evolution. We would then discuss the Nested Evolutionary Strategies where we fix the parameters of an Evolutionary Strategy by some technique. Towards the end we discuss the Hybrid Genetic Algorithm Particle Swarm Optimization. This algorithm generates individuals by twin usage of Genetic Algorithm and Particle Swarm Optimization.