Cultural Algorithm Research Articles

Cultural-Based Genetic Tabu Algorithm for Multiobjective Job Shop Scheduling

The job shop scheduling problem, which has been dealt with by various traditional optimization methods over the decades, has proved to be an NP-hard problem and difficult in solving, especially in the multiobjective field. In this paper, we have proposed a novel quadspace cultural genetic tabu algorithm (QSCGTA) to solve such problem. This algorithm provides a different structure from the original cultural algorithm in containing double brief spaces and population spaces. These spaces deal with different levels of populations globally and locally by applying genetic and tabu searches separately and exchange information regularly to make the process more effective towards promising areas, along with modified multiobjective domination and transform functions. Moreover, we have presented a bidirectional shifting for the decoding process of job shop scheduling. The computational results we presented significantly prove the effectiveness and efficiency of the cultural-based genetic tabu algorithm for the multiobjective job shop scheduling problem.

Studies on Economic Feasibility of an Autonomous Power Delivery System Utilizing Alternative Hybrid Distributed Energy Resources

An economic evaluation of a network of distributed energy resources (DERs) forming an autonomous power delivery system in an Indian scenario has been made. The mathematical analysis is based on the application of a real valued cultural algorithm (RVCA). The RVCA-evaluated total annual costs for the autonomous microgrid system utilizing both solar module and fuel cells as DERs and solar module and bio-mass gassifier unit as DERs have been compared. Different types of consumers together form a microgrid with the optimal supply of power from DERs. The optimal power generation conditions have been obtained pertaining to minimum cost of microgrid system. The results for different loading scenarios, using hybrid solar-biomass gassifier unit are found to be more cost competitive. A reduction of 8.1% in the annual cost is obtained using solar module-biomass gassifier unit to that using solar module-fuel cell for the same load demand in microgrid operation.

Monitoring the Vital Signs in a Complex Social System

It has been conjectured that “Culture is an optimizing process”. That is, the social structures that emerge in a given organization support the efficient solution of problems that the organization must deal with. Here we will present an escalating sequence of static problems to our system. Our goal will be to study how the social network infrastructure affects the overall performance of social system of a given complexity. We develop a Cultural Engine metaphor in terms of measures used to describe the entropy of each of the Cultural Algorithm components. The influence function worked analogous to Maxwell's Demon in order to inject new entropy into the system so as to counteract the effect of the second law of thermodynamics. We then applied the system to the solution of a 150 randomly generated problems of varying complexity produced by a problem generation system based upon complexity theory.

Cultural algorithms: a Tabu search approach for the optimization of engineering design problems

Cultural Algorithms and Tabu search algorithms are both powerful tools to solve intricate constrained engineering and large-scale multi-modal optimization problems. In this paper, we introduce a hybrid approach that combines Cultural Algorithms and Tabu search (CA---TS). Here, Tabu Search is used to transform History Knowledge in the Belief Space from a passive knowledge source to an active one. In each generation of the Cultural Algorithm, we calculate the best individual solution and then seek the best new neighbor of that solution in the social network for that population using Tabu search. In order to speed up the convergence process through knowledge dissemination, simple forms of social network topologies were used to describe the connectivity of individual solutions. This can reduce the number of needed generations while maintaining accuracy and increasing the search radius when needed. The integration of the Tabu search algorithm as a local enhancement process enables CA---TS to leap over false peaks and local optima. The proposed hybrid algorithm is applied to a set of complex non-linear constrained engineering optimization design problems. Furthermore, computational results are discussed to show that the algorithm can produce results that are comparable or superior to those of other well-known optimization algorithms from the literature, and can improve the performance and the speed of convergence with a reduced communication cost.

Power loss minimization by optimal capacitor placement in radial distribution system using modified cultural algorithm

Summary A new evolutionary technique named modified cultural algorithm (MCA) has been implemented for the reduction of real power loss by optimal capacitor allocation. This evolutionary algorithm has provided promising results for 9-bus, 34-bus, and 69-bus standard distribution systems dealing with the real power loss. It has been observed that situation and history knowledge influence the population space with the updating of belief space. Comparative study has been done to establish the effectiveness of MCA with other methods such as cultural algorithm, heuristic, fuzzy-logic, and hybrid methods. The real power loss has been observed to be reduced from 783.8 to 677.54 kW and from 221.5 to 160.9 kW for 9-bus and 34-bus distribution systems, respectively. It is also shown that MCA has outperformed cultural algorithm as well as real-coded genetic algorithm and particle swarm optimization for loss as well as cost reduction in 69-bus distribution system. Copyright © 2013 John Wiley & Sons, Ltd.

Application of cultural algorithm in cross-docking scheduling

This paper studied on the operational scheduling problem in a cross-docking center of a single receiving and a single shipping door with finite temporary storage.A dynamic programming model was built with the objective to minimize the costs including additional handing,temporary storage and truck replacement cost.A cultural algorithm with two layer evolutionary mechanism was proposed to solve the problem.The evolution of population space adopted genetic algorithm,and the belief space received good individual from population space to form knowledge which was used to guide evolution in turn.Numerical experiments under small and big scale situations prove the validity of proposed cultural algorithm.

Ancient Jing De Zhen Dong He River Basin Kiln and Farmland Land-use Change Based on Cellular Automata and Cultural Algorithm Model

The aim of this study is to understand how farmland has transformed kiln in ancient Jing De Zhen Dong He River Basin; we created ancient virtual maps of study area and conducted a series of spatial analyses of the land- use pattern from the Yuan Dynasty to the Ming Dynasty. The results of the spatial analysis show that kiln can evolve from farmland, shrub, idle land etc. To simulate land-use change we developed a novel cellular automata model. Model parameters and neighborhood rules were obtained with the cellular automata model melt modified cultural algorithm. Virtual land-use maps from the Yuan Dynasty to the Ming Dynasty were used to implement the model with a time step of one year. Model performance was evaluated using Moran's I index estimation for selected landscape pattern indices. The optimized parameter set using Particle Swarm Optimization poorly simulated land- use change as compared to the optimized parameter set using Cultural Algorithm. In summary, our results proved that the model is also effective and feasible in simulating farmland and kiln land-use evolution in ancient times when Geographic Information and System information were lacking.

Substation Locating and Sizing in Distribution Network Based on Geographic Information

For substation locating and sizing in the distribution network, the cost of land and geographic information should be considered. A new algorithm based on cultural differential evolution algorithm (CDEA) considering geographic information factor is presented. Based on the improved technique for order preference by similarity to ideal solution (ITOPSIS), geographic information factor model is set up. CDEA is proposed by designing three kinds of knowledge in belief space to guide evolution. The new algorithm overcomes the premature phenomenon of differential evolution algorithm and improves significantly ability of global optimization. Finally the case proves that the proposed model and method is correct, having certain practical value.

Interventions via Social Influence for Emergent Suboptimal Restraint Use

Although restraint use has increased primarily in developed countries, vehicle accident-related injuries and deaths continue to be a problem. Alongside lack of restraint use, studies involving suboptimal restraint use have gained recent popularity. In this study we investigate the use of social influence forinterventions to counter emerging suboptimal restraint use in groups of agents.A multi-agent simulation model is provided where dominant individuals use randomly assigned influence rates to repeatedly alter the knowledge of lessinfluential group members. Cultural influence is implemented via a cultural algorithm and used to simulate individuals affected by beliefs in the community. Objectives include investigating the emergence of patterns of restraint selection and use as well as interventions targeted at more influential agents. Results demonstrate that prominent patterns of behaviour similar to the influentialmembers of the groups do emerge. Furthermore, interventions targeted at influential group members outperform interventions targeted at a percentage of the population at large. Interventions succeed at some level both in the presence and absence of cultural influence.

An optimization algorithm inspired by musical composition in constrained optimization problems

Many real-world problems can be expressed as an instance of the constrained nonlinear optimization problem (CNOP). This problem has a set of constraints specifies the feasible solution space. In the last years several algorithms have been proposed and developed for tackling CNOP. In this paper, we present a cultural algorithm for constrained optimization, which is an adaptation of “Musical Composition Method” or MCM, which was proposed in [33] by Mora et al. We evaluated and analyzed the performance of MCM on five test cases benchmark of the CNOP. Numerical results were compared to evolutionary algorithm based on homomorphous mapping [23], Artificial Immune System [9] and anti-culture population algorithm [39]. The experimental results demonstrate that MCM significantly improves the global performances of the other tested metaheuristics on same of benchmark functions.

An evolutionary heuristic algorithm for the assignment problem

An implementation of evolutionary heuristic algorithm, which is a specific modified variant of cultural algorithm, with simplified computational procedure is presented here for solving assignment problems. A cultural algorithm consists a population component almost identical to that of the genetic algorithm and, in addition, a knowledge component called the belief space. The major constraint of assignment problems that a single job can be assigned to only one resource and the resource or job that involves the largest and as well as the least cost are availed as the knowledge components to be used to build belief space.

A Novel Method for Mining Association Rules from Continuous Attributes Based on Cultural Immune Algorithm

It is an important topic to mine association rules from data with continuous attributes. A novel model based on cultural algorithm and immune algorithm is proposed in this paper. This algorithm uses the cultural algorithm framework in which the immune algorithm embedded. The immune algorithm is used to discretize the continuous attributes and search association rules in the database rapidly. The cultural algorithm is used to obtain the commonly accepted beliefs in order to guide and speed up the search. The new algorithm integrates the discretization, attributes reduction and association rules mining together. In addition, a new diversity operator is put forward in order to discover global solutions. The experiment shows that the new algorithm is superior to immune algorithm in convergence speed and the rule’s accuracy.

Cultural Ant Algorithm for Continuous Optimization Problems

In order to overcome prematurity of ant colony algorithm, the conception of belief space originated in cultural algorithm is introduced, and a new cultural ant algorithm is proposed for continuous optimization problems. Firstly, the coding scheme for ant colony algorithm to solve continuous optimization problems is discussed. Then belief space is brought in, and designed as the form of two parts: individual belief space and population belief space. The former is used to conduct individuals' deep search for better solutions, and the other to help worse individuals drop their current bad solution space for broad search. The update rules of both population space and belief space are given subsequently. Eight common standard functions are used to test the new algorithm, which is compared with four other algorithms at the same time. The results show effectiveness and superiority of the new algorithm. Finally the effect of the parameter used in the algorithm is discussed, and so does the both two belief space.

A Discretization Algorithm Based on Cultural Immune Algorithm

Now more and more researchers pay attention to discretization of the continuous attributes. A hybrid evolutionary system based on immune algorithm and cultural algorithm is proposed for discretization which combines the strengths of evolutionary computing, social computing in this paper. It uses the cultural algorithm framework in which the immune algorithm embedded. Immune algorithm discretizes the continuous data and the cultural algorithm constitutes the commonly accepted beliefs to guide and speed up the search. In addition, a new diversity operator is put forward in order to maintain the diversity of population. Finally, experiments are conducted for extensive performance testing of the new method on several publicly available data sets. The results show that the algorithm has higher convergence speed and less classification errors than many previously known discretization methods.

A Resource Allocation Algorithm in RFID System

For problem of limited resources in the RFID (Radio frequency identification) system, a power resource allocation scheme is proposed. The method aims to maximize the system throughput, using cultural algorithm (CA) to search for the optimal power allocation scheme. By dynamically adjusting the signal transmission power of the reader, the overlap area between the reader can be reduced so that the maximum reading range can be obtained. Simulation results show that the algorithm has better performance in the system throughput and energy consumption, reducing the impact of interference between the readers and efficiently using the resources in RFID system.

Optimization problem solving with auctions in Cultural Algorithms

In this paper we investigate the role that the competition among knowledge sources plays in the problem solving behavior of a population as the complexity of the optimization problem solving landscapes increases. We employ a type of game theoretic mechanism, auctions, in our study. Our main goal is to determine whether it matters if knowledge sources competed for individuals using auction mechanisms or weighted majority win mechanisms as the landscape complexity increased. The weighted majority win situation allows the Knowledge Sources to make predictions about future success whereas the Auction mechanism allows them to invest in their future by using tokens earned from recent performance. This latter approach allows contextual Knowledge Sources such as Situational Knowledge to play a larger role. Although the results are preliminary, it appears that the auction mechanism is more efficient when solving a problem in situations where contextual information is available. In that case, it is easier for the knowledge sources to make judicial bids or investments. However, once the landscapes become chaotic there is less contextual information available and correspondingly little advantage to the auction mechanism over the weighted majority win situation in terms of the number of generations needed to achieve a solution.

Using Virtual Worlds to Facilitate the Exploration of Ancient Landscapes

The Land Bridge – Cultural Algorithms Program Simulation (L-CAPS) system is a functional interface for learning group behavior using Cultural Algorithms (CA). It enables the Cultural Algorithms process to implicitly communicate with, modify, and evaluate autonomous game agents restricted to an external virtual world. The L-CAPS system extends the work of Kinnaird-Heether (Reynolds & Kinnaird-Heether, 2010), and is able to examine the viability of using CA in learning group behavior in games, as well as the ability for CA to be extended to more general game designs. Here it is applied to the learning of large group movement for the Land Bridge reality game. The goal of the Land Bridge Game is to recreate the virtual world of an ancient land bridge that extended across modern Lake Huron between 10,000 B.C. and 7000 B.C. Here, the authors are trying to predict the location of hunting sites, assuming that the positioning of those sites is related to the distribution of available game. In this paper they use L-CAPS to tune an algorithm to simulate the movement of large herds of caribou. In subsequent work the authors will then generate optimal paths for these herds through the ancient landscape using various path planning algorithms.

Cost Minimization of Power Delivery Systems by Optimal Power Generation from Distributed Energy Resources Using Real Valued Cultural Algorithm

An economic estimation of a network of distributed energy resources (DERs) forming a power delivery system in an Indian scenario has been made. The mathematical analysis is based on the application of real valued cultural algorithm (RVCA). The RVCA-evaluated total annual cost for power delivery networks has been obtained utilizing both solar and fuel cells as DERs along with a battery energy storage system. The analyses for optimal power operation pertaining to minimum cost have been made by considering two cases in Indian power delivery system. The first case deals with the consumers’ individual optimal operation of DERs, while in the second one, the consumers form a microgrid with the optimal supply of power from DERs. The total annual costs for these two cases are found to be economically competitive and encouraging in the context of supplying reliable power to a small locality. A reduction of approximately 4.3% in the annual cost has been obtained in the case of microgid system than that in the separately operating consumers’ system for a small locality of India. It is observed that the application of RVCA in power optimization results in a reduction of total cost depicting an improved outcome in terms of energy economy.

Economic environmental dispatch using an enhanced multi-objective cultural algorithm

Economic environment dispatch (EED) is a significant optimization problem in conventional fossil fuel fired power system to distribute load demand reasonably and scientifically so that fuel cost and emission issues are optimized simultaneously while satisfying various constraints. Valve-point effect, prohibited operation zones (POZs) and transmission losses make the EED a non-smooth and non-convex optimization problem. In order to solve this constrained multi-objective optimization problem including competing objectives as well as complex constraints, an enhanced multi-objective cultural algorithm (EMOCA) is proposed in this paper. The proposed method combines cultural algorithm framework with particle swarm optimization (PSO) to carry though the evolution of population space. Besides, two knowledge structures of belief space in CA are redefined according to the features of EED problem. Moreover, an effective constrain handling method is proposed to handle the equality and inequality constraints of EED problem. Simulation results of test systems demonstrate the capability of the proposed algorithm to generate well-distributed Pareto optimal solutions in a single run. Compared with some current methods, EMOCA has a good performance in finding a diverse set of solutions and in converging near the true Pareto optimal front with lower fuel cost and emission issues synthetically.

Circuit Optimization Design based on Improved Cultural Algorithm

Circuit Optimization Design based on Improved Cultural Algorithm