EP-FPG applied to RSSI-Based Wireless Indoor Localization

— 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.

The history matching, a key step in the reservoir numerical simulation, is performed to obtain a set of appropriate reservoir parameters to match the modeling results with the oilfield historical production data. The history matching problem is solved conventionally with Gauss-Newton, L-M, BFGS, and LBFGS methods, which require calculation of the gradient of the objective function, and is very slow in solving large-scale problems. In recent years, modern optimization algorithms, such as the genetic algorithm, particle swarm optimization, and differential evolution algorithm, have been applied in the reservoir history matching. In this paper, the hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) is used to carry out the research on the automatic reservoir history matching. First, the basic concepts of the particle swarm optimization (PSO) and the gravity search algorithm (GSA) were introduced. Then, the concept and algorithm of PSOGSA were described. Moreover, the actual oil field data were used to establish a geological model and an objective function, and the PSOGSA was used to carry out the automatic history matching. The PSOGSA was compared with other algorithms. Finally, the effects of parameters on the PSOGSA were analyzed. The results show that the PSOGSA has a good performance in the automatic reservoir history matching, increases the diversity of particles, improves the global search capability, and accelerates the convergence. The PSOGSA is superior to the PSO or the GSA.

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

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.

During recent decades, using wind power as one of the most substantial renewable energy sources has been vastly intensified in power systems. Increasing wind power penetration in the power grid can have both detrimental and beneficial effects. Due to the uncertain nature of wind power, the system operator is faced with many challenges to make proper decisions for the electricity market. In this paper, a probabilistic method is proposed which not only effectively manages wind power sources along with other producers, but also takes the uncertain behavior of wind power in defined intervals into account. Using wind farm information in each hour, a new approach for calculating the probability density function (PDF) in uncertainty bounds is presented. By utilizing the calculated PDF two objective functions are proposed for minimizing the total expected cost during 24 h without and with considering consumers payments. The proposed objective functions have been minimized through a two-level optimization method using three different heuristic algorithms (Teaching-Learning-Based Optimization (TLBO), Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA), and Ant Lion Optimization (ALO)) on an 8-bus sample transmission network. The results show the total expected cost has been decreased up to 20% in some scenarios. In addition, the proposed method can satisfy consumers by keeping their payments close to the lowest amount according to the different scenarios.

Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization

In this paper, we present a parallel machine scheduling with shared and multi-mode resources (PMS-SMR) problem which is widely encountered in manufacturing engineering. The integer programming for this problem is given and a hybrid particle swarm optimization (HPSO) algorithm is put forward with a problem-based solution representation and a balancing optimization (BO)-based local search. The HPSO algorithm can be applied to obtain the best permutation of the fitness function with least-makespan optimization criterion. Experimental results show that it is feasible and effective, especially in solving large-scale problems.

In this paper, we present a Hybrid Particle Swarm Optimization (HPSO) algorithm that combines the Particle Swarm Optimization (PSO) algorithm with the Genetic Algorithm (GA) method. The algorithm is used for investigating the plastic deformation behavior of titanium alloy (Ti-6Al-4V) in a multi-hole extrusion process. The simulation used rigid-plastic finite element (FE) DEFORMTM-3D software to obtain the minimum mandrel bias angle and exit tube bending angle. Results of the simulation indicate that these two angles were significantly less than 0.3 degrees, suggesting that metaheuristic algorithms based on HPSO and FE analysis could be used for general multi-hole extrusion processes.KeywordsMulti-hole extrusionParticle Swarm OptimizationFinite elementGenetic Algorithm

In the renewable energy sector, the extraction of parameters for solar photovoltaic (PV) cells is a widely studied area of research. Parameter extraction is a non-linear complex optimization problem for solar PV cells. In this research work, the authors have implemented the Tunicate swarm algorithm (TSA) to estimate the optimized value of the unknown parameters of a PV cell/module under standard temperature conditions. The simulation results have been compared with four different, pre-existing optimization algorithms: gravitational search algorithm (GSA), a hybrid of particle swarm optimization and gravitational search algorithm (PSOGSA), sine cosine (SCA), and whale optimization (WOA). The comparison of results broadly demonstrates that the TSA algorithm outperforms the existing optimization algorithms in terms of root mean square error (RMSE) and convergence rate. Furthermore, the statistical results confirm that the TSA algorithm is a better algorithm in terms of average robustness and precision. The Friedman ranking test is also carried out to demonstrate the competency and reliability of the implemented approach.

<strong class="journal-contentHeaderColor">Abstract.</strong> In this paper, we have developed the Matlab code for a weighted hybrid of particle swarm optimization (PSO) and gravitational search algorithm (GSA) known as wPSOGSA, GSA, and PSO algorithms to interpret one-dimensional magnetotelluric (MT) data for some corrupted and non-corrupted synthetic data, as well as two examples of MT field data over different geological terrains: (i) geothermal rich area, Island of Milos, Greece, and (ii) Southern Scotland due to the occurrence of a significantly high electrical conductivity anomaly under crust and upper mantle extending from the Midland Valley across the Southern Uplands into northern England. Even though the fact that many models provide a good fit in a large predefined search space, specific models do not fit well. As a result, we used a Bayesian statistical technique to construct and assess the posterior probability density function (PDF) rather than picking the global model based on the lowest misfit error. This is proceeded by 68.27 % confidence interval for selecting a region where PDF is more prevalent to estimate the mean model which is more accurate and close to the true model. For illustration, correlation matrices show a significant relationship among layer parameters. The findings indicate, the wPSOGSA is less sensitive to model parameters and produces well, more stable and reliable results with the least uncertainty in the model that is compatible with existing borehole samples. Furthermore, the present methods resolve two additional geologically significant layers, one highly conductive (less than 1.0 Ωm) and another resistive (300.0 Ωm) over the Island of Milos, Greece, characterized by alluvium and volcanic deposits, respectively, as corroborated by borehole stratigraphy.

Hybrid particle swarm optimization algorithm for scheduling flexible assembly systems with blocking and deadlock constraints

This paper presents the development and application of hybrid particle swarm optimization and gravitational search algorithm (PSOGSA) using cooperative technique (CPSOGSA). In this work, the algorithm is used for functions optimization problem. The CPSOGSA is developed with multiple groups using master-slave architecture. Six benchmark functions were tested to verify the effectiveness of the optimization algorithm. The developed algorithm was compared with the existing PSO, GSA and hybrid PSOGSA. The results indicate that the proposed technique produces better optimization solution compared to the conventional optimization algorithms.

Network-on-chip (NoC) has been proposed as a solution for the global communication challenges of system-on-chip (SoC) design in the nanoscale technologies. In this paper, a methodology is presented to develop an efficient routing algorithm for network-on-chip platforms that are specialized for an application or a set of concurrent applications. The proposed routing methodology, based on the hybrid particle swarm optimization (PSO) Algorithm, is applied on the 2D-mesh NoC platform to balance the link load. Experimental results show that this routing algorithm can efficiently assign deterministic, deadlock-free, minimal routing paths for traffic traces in a short period of time, and significantly guarantee the bandwidth requirement. In addition, the Hybrid PSO is combined with the operations of GA Algorithm, so that the algorithm achieves better performance.

A large project in a company is often divided to several subtasks, which would be assigned to different people with variant abilities to the same task. So whether the tasks are scheduled properly would determine the quality or the efficiency of team collaboration. A hybrid particle swarm optimization (PSO) algorithm is putted forward. Subtasks are disassembled from the project by using the task tree relations, and the tree structure is modeled into a task matrix. Moreover, task-time matrix is used to indicate the people abilities to complete the tasks. Then the hybrid algorithm was presented, in which simulated annealing method is added in particle swarm optimization to improve the capability of seeking the best allocating results. Finally, a simulation experiment is carried out by using the proposed algorithm, the comparing results show that the convergent velocity is fast and the optimizing ability is preferable.

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.