A Hierarchical Evolutionary Algorithm with Noisy Fitness in Structural Optimization Problems

2005 ◽  
pp. 610-616
Author(s):  
Ferrante Neri ◽  
Anna V. Kononova ◽  
Giuseppe Delvecchio ◽  
Marcello Sylos Labini ◽  
Alexey V. Uglanov
Keyword(s):  
Structural Optimization ◽  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Noisy Fitness

scholarly journals Performance of the Modified Dolphin Monitoring Operator for Weight Optimization of Skeletal Structures

2018 ◽  
Author(s):  
Ali Kaveh ◽  
S.R. Hoseini Vaez ◽  
Pedram Hosseini
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Structural Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Harmony Search ◽  
Average Weight ◽  
Weight Optimization ◽  
Swarm Optimization ◽  
Population Dispersion

In this study, the Modified Dolphin Monitoring (MDM) operator is used to enhance the performance of some metaheuristic algorithms. The MDM is a recently presented operator that controls the population dispersion in each iteration. Algorithms are selected from some well-established algorithms. Here, this operator is applied on Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Vibrating Particles System (VPS), Enhanced Vibrating Particles System (EVPS), Colliding Bodied Optimization (CBO) and Harmony Search (HS) and the performance of these algorithms are evaluated with and without this operator on three well-known structural optimization problems. The results show the performance of this operator on these algorithms for the best, the worst, average and average weight of the first quarter of answers.

Optimum Oil Production Planning Using Infeasibility Driven Evolutionary Algorithm

2013 ◽  
Vol 21 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Hemant Kumar Singh ◽  
Tapabrata Ray ◽  
Ruhul Sarker
Keyword(s):  
Evolutionary Algorithm ◽  
Production Planning ◽  
Oil Recovery ◽  
Optimization Problems ◽  
Nonlinear Function ◽  
Practical Interest ◽  
Oil Production ◽  
Economic Benefits ◽  
Financial Benefit ◽  
Single Objective

In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.

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