Hybrid GA-PSO Approach Research Articles

Development of an efficient hybrid GA-PSO approach applicable for well placement optimization

When it comes to the economic efficiency of oil and gas field development, finding the optimum well locations that augment an economical cost function like net present value (NPV) is of paramount importance. Well location optimization has long been a challenging problem due to the heterogeneous nature of hydrocarbon reservoirs, economic criteria, and technical uncertainties. These complexities lead to an enormous number of possible solutions that must be evaluated using an evaluation function (e.g. a simulator). This makes it necessary to develop a powerful optimization algorithm into which a fast function evaluation tool is incorporated. The present study describes the application of a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) into a hybrid GA-PSO algorithm that is implemented in a streamline simulator to determine optimal locations for production and injection wells across heterogeneous reservoir models. Performance of the hybrid GA-PSO algorithm is then compared to that of the PSO and the GA separately. The results confirm that compared to conventional methods, the recommended method provides a fast and well-defined approach for production optimization complications. Cited as : Yazdanpanah, A., Rezaei, A., Mahdiyar, H., Kalantariasl, A. Development of an efficient hybrid GA-PSO approach applicable for well. Advances in Geo-Energy Research, 2019, 3(4): 365-374, doi: 10.26804/ager.2019.04.03

A hybrid GA-PSO approach for reliability under type II censored data using exponential distribution

A hybrid GA-PSO approach for reliability under type II censored data using exponential distribution

Fuzzy Enabled Hybrid Genetic Algorithm–Particle Swarm Optimization Approach to Solve TCRO Problems in Construction Project Planning

AbstractOne of the most challenging tasks of a construction project planner is to simultaneously minimize the total project cost and total project duration while considering issues related to optimal resource allocation and resource leveling. Therefore, project planners face complicated multivariate, time-cost-resource optimization (TCRO) problems that require time-cost-resource tradeoff analysis. The hybrid GA–PSO approach is presented to solve complex, TCRO problems in construction project planning. The proposed approach uses the fuzzy set theory to characterize uncertainty about the input data (i.e., time, cost, and resources required to perform an activity) in this hybrid approach. The proposed fuzzy-enabled hybrid GA–PSO approach is applied to solve two optimization problems that are found in the construction project planning literature. It is shown that the proposed fuzzy enabled hybrid GA–PSO approach is superior to existing optimization algorithms at finding better project schedule solutions with ...

Inferring gene regulatory networks using a hybrid GA–PSO approach with numerical constraints and network decomposition

Gene regulatory networks (GRNs) are essential for cellular metabolism during the development of living organisms. Reconstructing gene networks from expression profiling data can help biologists generate and test hypotheses to investigate the complex phenomena of nature systems. However, building regulatory models is a tedious task, especially when the number of genes and the complexity of regulation increase. To automate the procedure of network reconstruction, we establish a methodology to infer the computational network model and to deal with the problem of scalability from two directions. The first is to develop an enhanced GA–PSO hybrid method to search promising solutions, and the second is to develop a network decomposition procedure to reduce the task complexity. Meanwhile, our work includes a quantitative method to consider prior knowledge in the inference process to ensure validity of the obtained results. Experiments have been conducted to evaluate the proposed approach. The results indicate that it can be used to infer GRNs successfully and can achieve better performance.

A hybrid algorithm for uncapacitated facility location problems

Location problems are NP-hard combinatorial optimisation problems which are widely studied. This paper presents a hybrid algorithm to solve the Uncapacitated Facility Location (UFL) problems. Denoted as hGA-PSO, this method combines the concepts of Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) Algorithm, and creates individuals not only by using operations used in GA, but also by mechanisms of PSO. The proposed algorithm is applied to several benchmark suites collected from OR-library. These studies demonstrate the ability of the hybrid GA-PSO approach to give good solutions for the UFL problems. The performance of the algorithm is also compared with other approaches available in the literature.

A hybrid genetic algorithm and particle swarm optimization for multimodal functions

Heuristic optimization provides a robust and efficient approach for solving complex real-world problems. The focus of this research is on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), for the global optimization of multimodal functions. Denoted as GA-PSO, this hybrid technique incorporates concepts from GA and PSO and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by mechanisms of PSO. The results of various experimental studies using a suite of 17 multimodal test functions taken from the literature have demonstrated the superiority of the hybrid GA-PSO approach over the other four search techniques in terms of solution quality and convergence rates.