Cultural Algorithm Research Articles

Numerical simulation and optimization of steam-assisted gravity drainage with temperature, rate, and well distance control using an efficient hybrid optimization technique

ABSTRACTSteam-assisted gravity drainage (SAGD) is a thermal enhanced oil recovery technique through drilling of two horizontal wells. Effects of steam injection temperature, well rates, and their distance on oil recovery were analyzed and optimized. Steam temperature and well distances remarkably affect SAGD performance. Four metaheuristic algorithms (particle swarm optimization (PSO), imperialist competitive algorithm, cultural algorithm, and Bees algorithm) and pattern search optimization algorithm (PSA) are used for optimization. PSO performs better than other metaheuristics and PSA is the fastest one, while it is probable to be trapped in local optimums. Hybrid PSO-PS is proposed that starts with PSO and proceeds with PSA, and tested in an SAGD project and showed excellence over other techniques.

Predicting tensile strength of rocks from physical properties based on support vector regression optimized by cultural algorithm

The tensile strength (TS) of rocks is an important parameter in the design of a variety of engineering structures such as the surface and underground mines, dam foundations, types of tunnels and excavations, and oil wells. In addition, the physical properties of a rock are intrinsic characteristics, which influence its mechanical behavior at a fundamental level. In this paper, a new approach combining the support vector regression (SVR) with a cultural algorithm (CA) is presented in order to predict TS of rocks from their physical properties. CA is used to determine the optimal value of the SVR controlling the parameters. A dataset including 29 data points was used in this study, in which 20 data points (70%) were considered for constructing the model and the remaining ones (9 data points) were used to evaluate the degree of accuracy and robustness. The results obtained show that the SVR optimized by the CA model can be successfully used to predict TS.

Reconfiguration of Distribution Network Using Cultural Algorithm for Loss Reduction

Reconfiguration of Distribution Network Using Cultural Algorithm for Loss Reduction

Contaminant source identification of water distribution networks using cultural algorithm

SummaryIn recent years, the drinking water pollution incident occurred frequently, a serious threat to social stability and security. By using the sensor networks real‐time monitoring the urban water supply networks, the water pollution event probability can be greatly reduced. But knowing how to use the water quality monitor sensor networks to collect information to identify pollution sources is still a challenging problem. In this paper, we formulate the contaminant source identification problem into an optimization problem, and then design the cultural algorithm to solve it by considering different sizes of water supply networks as the experimental data. Finally, the experimental results verify the effectiveness and robustness of the proposed method.

Integration in industrial automation based on multi-agent systems using cultural algorithms for optimizing the coordination mechanisms

Integration in industrial automation can be approached from the theory of Distributed Artificial Intelligence. One approach is the modeling of different production units by agents that interact through interaction protocols, which are implemented following a coordination mechanism. Under this approach, integration in automation can be achieved through the optimization of implicit interactions in such mechanisms. This paper presents a strategy for integrating industrial processes based on Multi-Agent Systems (MAS), which consists of optimizing coordination mechanisms that implement conversations between agents, by using cultural algorithms. The cultural algorithm uses formal models of interaction protocols between agents, such as auction and tender, and the integration scheme comes from automation architectures based on MAS, to which their interactions are optimized. The proposed scheme enables data and service-oriented integration. The proposed strategy is applied in two industrial case studies related to the oil production process.

Optimisation of Cognitive Engine Design using Cultural Algorithm

Objectives: To optimize the design of cognitive radio engine using Cultural Algorithm (CA). The simulated results are compared with commonly used Evolutionary Algorithms (EA) like Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). Methods/Statistical Analysis: Use of CA has been proposed to find a suitable fitness score under varying channel conditions over multiple iterations. Matlab has been chosen as the platform for simulating various scenarios. An attempt has also been made to optimize the time of convergence. Findings: Simulations indicate that CA emerges as a potential candidate for designing of CR Engine (CRE) for deployment of a CR Network (CRN). Using CA, the fitness score has improved as compared to other EAs. Improvements: The algorithm shows faster convergence and improves its performance with each successive iteration.

Technical and economic analysis of home energy management system incorporating small-scale wind turbine and battery energy storage system

Home energy management system (HEMS) is an important problem that has been attracting significant attentions in the recent years. However, the conventional HEMS includes several shortcomings. The conventional HEMSs mainly utilize battery energy storage system (BESS) to deal with energy uncertainties. But they only ascertain optimal charging-discharging pattern for BESS and the power and capacity of BESS are not optimally determined. Furthermore, most of the HEMSs are modeled as a mixed integer linear programming (MILP) including linearization and relaxations. Additionally, considering all possible operating conditions for home has not been adequately addressed in the existing HEMSs. The possible operating conditions are (i) receiving energy from the main grid (i.e., purchasing energy), (ii) sending energy to the utility grid (i.e., selling energy), (iii) working on standalone mode as disconnected from the network (i.e., net-zero energy building). As a result, current paper deals with these existing challenges at the same time. This paper presents HEMS including small-scale wind turbine, BESS, load curtailment option, and fuel cell vehicle. The introduced HEMS not only determines optimal charging-discharging pattern for BESS, but also specifies optimal capacity and optimal rated power of the BESS at the same time. The proposed HEMS is expressed as a mixed integer nonlinear programming (MINLP) and solved by cultural algorithm as an effective Meta-heuristic optimization algorithm. All three operating conditions are considered for home. Output power of wind unit is modeled by Gaussian probability distribution function (PDF) and Monte-Carlo simulation (MCS) is applied to deal with uncertainties. Results emphasize on the feasibility and usefulness of the introduced HEMS.

Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm

In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial conflicts iteratively. In this paper, we apply the immune genetic algorithm (IGA) and improved particle swarm optimization (PSO) to building displacement to solve conflicts. The dual-inheritance framework from the cultural algorithm is adopted in the PSO algorithm to optimize the topologic structure of particles. We generate Pareto optimal displacement solutions using the niche Pareto competition mechanism. The results of experiments comparing IGA and the improved PSO show that the improved PSO outperforms IGA; the improved PSO results in fewer graphic conflicts and smaller movements that better satisfy the movement precision requirements.

An Improved Multiobjective Particle Swarm Optimization Based on Culture Algorithms

In this paper, we propose a new approach to raise the performance of multiobjective particle swam optimization. The personal guide and global guide are updated using three kinds of knowledge extracted from the population based on cultural algorithms. An epsilon domination criterion has been employed to enhance the convergence and diversity of the approximate Pareto front. Moreover, a simple polynomial mutation operator has been applied to both the population and the non-dominated archive. Experiments on two series of bench test suites have shown the effectiveness of the proposed approach. A comparison with several other algorithms that are considered good representatives of particle swarm optimization solutions has also been conducted, in order to verify the competitive performance of the proposed algorithm in solve multiobjective optimization problems.

An Improved Artificial Bee Colony using Cultural Algorithm for Optimization Problem

An Improved Artificial Bee Colony using Cultural Algorithm for Optimization Problem

Detecting faulty sensors in an array using symmetrical structure and cultural algorithm hybridized with differential evolution

The detection of fully and partially defective sensors in a linear array composed of N sensors is addressed. First, the symmetrical structure of a linear array is proposed. Second, a hybrid technique based on the cultural algorithm with differential evolution is developed. The symmetrical structure has two advantages: (1) Instead of finding all damaged patterns, only (N–1)/2 patterns are needed; (2) We are required to scan the region from 0° to 90° instead of from 0° to 180°. Obviously, the computational complexity can be reduced. Monte Carlo simulations were carried out to validate the performance of the proposed scheme, compared with existing methods in terms of computational time and mean square error.

An improved cultural algorithm and its application in image matching

Cultural Algorithm (CA) are a class of computational models derived from observing the cultural evolution process in nature and is used to solve complex calculations of the new global optimization search algorithms. Aiming at the traditional cultural algorithm has poor precision and trap into local optimum of global optimization. In this paper, introduce the isolation niche technology into the traditional cultural algorithm. With improvements, the algorithm is less likely to trap in local optimum. According to the test of one set of benchmark function, the proposed algorithm has greater improvements than ordinal cultural algorithm in the aspects of convergence precision and stability. In this paper, introduce the proposed algorithm into the image matching problem, and the simulation test shows that the algorithm for image matching problem has made great effects in stability and convergence precision.

Cultural algorithm with local search evaluated through non-parametric statistical tests

Cultural algorithm with local search evaluated through non-parametric statistical tests

Optimal Design of PID Controller for Deep Space Antenna Positioning Using Weighted Cultural Artificial Fish Swarm Algorithm

This paper proposed a modification of the Artificial Fish Swarm Algorithm (AFSA) using adaptive behaviour base combination of normative and situational knowledge inherent in cultural algorithm. Four variations (wCAFSA_ Ns, wCAFSA_Sd, wCAFSA_NsSd and wCAFSA_NsNd) of the AFSA called weighted Cultural Artificial Fish Swarm Algorithm (wCAFSA) were then proposed with the hope of reducing the chance of falling into local minima by the standard AFSA. The performances of these were first evaluated using a collection of seven optimizations benchmark functions. Thereafter, all the variants were used to design an optimized PID controller for the dc motor of deep space antenna azimuth position control with the hope of minimizing the cost function and maximizing the steady state responses. Simulation results demonstrated the effectiveness of the algorithms in determining efficiently the optimized PID controller gains. Even though all the developed algorithms demonstrated their effectiveness, the wCAFSA_NsSd appears to be more efficient.

Balancing emergency message dissemination and network lifetime in wireless body area network using ant colony optimization and Bayesian game formulation

Abstract Nowadays, Wireless Body Area Network (WBAN) is emerging very fast and so many new methods and algorithms are coming up for finding the optimal path for disseminating emergency messages. Ant Colony Optimization (ACO) is one of the cultural algorithms for solving many hard problems such as Travelling Salesman Problem (TSP). ACO is a natural behaviour of ants, which work stochastically with the help of pheromone trails deposited in the shortest route to find their food. This optimization procedure involves adapting, positive feedback and inherent parallelism. Each ant will deposit certain amount of pheromone in the tour construction it makes searching for food. This type of communication is known as stigmetric communication. In addition, if a dense WBAN environment prevails, such as hospital, i.e. in the environment of overlapping WBAN, game formulation was introduced for analyzing the mixed strategy behaviour of WBAN. In this paper, the ant colony optimization approach to the travelling salesman problem was applied to the WBAN to determine the shortest route for sending emergency message to the doctor via sensor nodes; and also a static Bayesian game formulation with mixed strategy was analysed to enhance the network lifetime. Whenever the patient needs any critical care or any other medical issue arises, emergency messages will be created by the WBAN and sent to the doctor's destination. All the modes of communication were realized in a simulation environment using OMNet++. The authors investigated a balanced model of emergency message dissemination and network lifetime in WBAN using ACO and Bayesian game formulation.

Intelligent Wind Turbine Power Curve Modelling Using the Third Version of Cultural Algorithm (CA3)

third version of cultural algorithm (CA3), error analysis, mathematical modelling, quadratic Gaussian function, wind turbine generator power curve (WTGPC).The wind turbine generator power curve (WTGPC) gives the relationship between the wind speed and power output of the wind turbine at any given time. The power curves, which are usually provided by the manufacturer company, are mainly used in forecasting, energy planning and performance monitoring of wind turbines. The WTGPC model plays a significant role in the control and monitoring of wind farms as well as playing a role in the wind farms power injection to the grid. This paper presents a comprehensive analysis of several methods of modelling the WTGPC, with respect to four commercial wind turbines; 330, 900, 2000 and 3050 kW. In the first step, the proposed method of the study, based on quadratic Gaussian function, is compared to several developed mathematical models by using error measurement techniques including mean square error (MSE) and residual analysis. The accuracy of the proposed method has then been improved by means of the third version of cultural algorithm (CA3) through the optimization of the proposed method coefficients. The ultimate performance of the compared methods has been investigated by the normalized root mean squared error (NRMSE), where the proposed method of the study shows an excellent performance for modelling of wind turbine power curves.

Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators

Optimal scheduling of generating resources plays a significant role as a decision-making tool for power system operators in the liberalized and real-time electricity spot markets. The real-time scheduling of generating units will become a very complex task with respect to the instantaneous fluctuation of the load demand due to several demand response scenarios in the smart grid context. In this study, a hybrid mathematical method for the online scheduling of units based on the least square support vector machine (LSSVM) and the third version of cultural algorithm (CA3) has been presented, where the CA3 has been specifically employed to tune the adjusting parameters of LSSVM. For the training purpose of the proposed method, the optimal scheduling of the daily load curve for four different test systems and various physical and environmental constraints of generating units have been prepared by using a modified mixed integer quadratic programming (MIQP) to deal with non-convex behaviors of the test systems. A mean squared error (MSE) objective function has been used to reduce the prediction errors during the training process to enhance the precision and reliability of the results. A radial basis function (RBF) and the proposed LSSVM-CA3 were used to check the convergence process. A high accuracy of generator schedule predictions are demonstrated by comparing the results of the proposed method with those of artificial neural networks. From the results, it can be inferred that the method is highly compatible for real-time dispatching of generation resources in deregulated electricity markets.

Using Cultural and Social Beliefs in Language Games

Agreement on word-object pairing in communication depends on the intensity of the beliefs that gradually emerge in a society of agents, on the condition that no one was born with embedded knowledge. The agents search and exchange ideas about unknown word-object pairings, until they meet a consensus about what the object should be named. A language game is a social process of finding agreement on word-object pairings through communication in a multi-agent system. In this paper, a technique is proposed to discover the association between a word and the agents’ beliefs on an object using self-organizing maps and a cultural algorithm in a multi-hearer environment. A conceptual space is implemented, which stores the agent’s beliefs in three dimensions, represented by colors. The technique was evaluated for a variety of scenarios using four significant measures: coherence, specificity, success rate, and word size. The results showed that with the proposed method social agents can reach agreement fast and that their communication is effective.

CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization

Many real-world problems can be formulated as optimization problems. Such problems pose a challenge for researchers in the design of efficient algorithms capable of finding the best solution with the least computational cost. In this paper, a new evolutionary algorithm is proposed that combines the explorative and exploitative capabilities of two evolutionary algorithms, Cultural Algorithm (CA) and Differential Evolution (DE) algorithm. This hybridization follows the HTH (High-level Teamwork Hybrid) nomenclature in which two meta-heuristics are executed in parallel. The new algorithm named as CADE, manages an overall population which is shared between CA and DE simultaneously. Four modified knowledge sources have been used in proposed CA which are: topographical, situational, normative and domain. The role of the used acceptance function in belief space is to select the knowledge of the best individuals to update the current knowledge. A novel quality function is used to determine the participation ratio for both CA and DE, and then a competitive selection takes place in order to select the proportion of function evaluations allocated for each technique. This collaborative synergy emerges between the DE and CA techniques and is shown to improve the quality of solutions, beyond what each of these two algorithms alone. The performance of the algorithm is evaluated on a set of 50 scalable optimization problems taken from two sources. The first set of 35 came from existing benchmark sets available in the literature. The second set came from the 2014 IEEE Single Function optimization competition. The overall results show that CADE has a favorable performance and scalability behaviors when compared to other recent state-of-the-art algorithms. CADE's overall performance ranked at number 1 for each of the two sets of problems. It is suggested that CADE's success across such a broad spectrum of problem types and complexities bodes well for its application to new and novel applications.

Application of PSO and cultural algorithms for transient analysis of natural gas pipeline

System simulation is an essential step for the optimization and control of natural gas transmission networks. Transient simulation of natural gas networks is more accurate than steady state simulation, but it imposes the complexity of solving nonlinear PDE flow equations.In this paper, a novel approach based on using two meta-heuristic algorithms: (a) particle swarm optimization (PSO), (b) cultural algorithm (CA) is presented to simplify transient simulation of gas networks with known inlet and outlet pressures.PSO or CA estimates different values for the network inlet flow rates. Using each of these estimated values, both boundary conditions will be known at the network inlets and discretized flow equations can be linearized. These linear flow equations will be solved for the network inlet nodes. This procedure will be continued for the next nodes until reaching network outlets. Then the differences of calculated and actual network outlet pressures will be defined as a cost function or error. Eventually, these algorithms will obtain the optimum inlet flow rates which minimize the cost function. Thus, the calculated pressures and flow rates at different gas network nodes which are obtained by using these optimum inlet flow rates will be the true values. Our proposed approach reduces the complexity of solving network transient flow equations with the mentioned boundary conditions while simulation results confirm its accuracy and efficiency.