Cuckoo Search Genetic Algorithm Research Articles

An integrated cuckoo search-genetic algorithm for mining frequent itemsets

Mining frequent itemsets is a contemporary research topic in the data-mining domain. Identifying frequent itemsets has acquired its significance due to recognized objectives in the field of association mining. The frequent itemset mining problem focuses on identifying frequent patterns and correspondence between the itemsets in the transactional database. Frequent itemset mining problems are time consuming when dealing with huge amounts of data and extracting the maximum number of the frequent itemsets from a large search space is a challenging process. Since the transactional database size is enormous, a stochastic method is applied to find optimal solutions. In this article, an integrated approach to extract frequent itemsets is proposed based on the cuckoo search and genetic algorithm. This integrated methodology is performed by merging the reproduction process of cuckoo species with the operators such as crossover and mutation in GA. First, a strategy to diversify the population and to discover maximum frequent patterns in the search process, Second, a neighborhood exploration strategy is applied for recurrent solutions to improve the accuracy. Finally, the proposed integrated approach is validated with respect to the solution quality and convergence rate. The integrated approach is verified using standard data instances. The ICSGA approach efficiently discovers the frequent patterns. The comparison results of the integrated approach are eminently productive than the other approaches in the literature.

Development and performance validation of new parallel hybrid cuckoo search–genetic algorithm

In this work, a new hybrid cuckoo search and genetic algorithm optimization method using a novel adaptive penalty function was proposed to solve the economic dispatch (ED) problem in smart grid. Please check and confirm the edit made in article title. This method was also paralyzed in order to solve the problem within specific time suitable to solve Energy management Problems. Three improvements are achieved through this combination. First, parallelism allows further reduction of the execution time. Second, the hybridization of both cuckoo search and genetic algorithm methods allows better diversification and exploration of search space which increases the solution quality. Third, the new adaptive penalty function was developed to discard infeasible solutions and to choose near-optimal ones within a short time. The efficiency of the developed algorithm is proven theoretically and experimentally. Three scenarios are considered to prove experimentally the out-performance of the developed method: (1) the proposed method is compared with Cuckoo Search and Genetic Algorithm methods using a set of benchmark functions. (2) A comparative study is carried out by applying the method to the ED continuous problem optimization case study. (3) The method is compared with Cuckoo search to solve discrete demand side management problem, considering each consumer as an independent parameter. The performance evaluation was conducted using Matlab data parallelism library.

Multi-agent learning technique inspired from software engineering model for permutation coded GA

Hybrid genetic algorithms (HGAs) have recognized significant attention in recent years and being progressively more used to solve real-world problems. HGA is the forms of assimilation between genetic algorithm and other search optimization techniques to improve the overall performance of the genetic algorithm (GA). GA can be hybridized with many other bio-inspired heuristic algorithms such as particle swarm optimization, cuckoo search genetic algorithm, firefly algorithm-genetic algorithm, real coded genetic algorithm-artificial fish swarm algorithm, etc., multi-agent system (MAS) is a new paradigm for conceptualizing, designing and optimizing the solution models. Multi-agent learning concept improves the outcome of the MAS and the type of learning process involved in it plays a vital role. Incremental model is a well-known software development process in which the system yields better effect at the end of every cycle. In this paper, software engineering inspired incremental model based multi-agent learning model for permutation coded genetic algorithms has been proposed. One of the famous combinatorial hard problems of traveling salesman problem (TSP) is being chosen as the test bed and the experiments are performed on large sized benchmark TSP instances obtained from standard TSPLIB. The experimental results support that the proposed model perform better than existing best working initialization methods in terms of convergence rate, error rate and computation time.

A Methodology for Assembly Sequence Optimization by Hybrid Cuckoo-Search Genetic Algorithm

In this paper, we have proposed and implemented a methodology for assembly sequence optimization by using a nature-inspired metaheuristic algorithm, known as hybrid cuckoo-search genetic algorithm (CSGA). The cost criteria for optimization in the present formulation takes into consideration the total assembly time and the number of reorientations during the assembly process. To demonstrate the application of the CSGA, an example assembly containing 19 parts has been presented and the results have been compared with those of another metaheuristic algorithm, Genetic Algorithm (GA). From the results, it has been observed that for the given problem, the CSGA not only produces optimal assembly sequences with costs comparable to that of GA, but the convergence of CSGA algorithm has been found to be faster than the GA algorithm.

A Hybrid Artificial Neural Network with Metaheuristic Algorithms for Predicting Stock Price

ABSTRACTMost investors change stock prices in long-term businesses because of global turbulence in the markets. Consequently, prediction of stock price is a difficult task because of unknown effective factors in this area although previous researches have shown that neural networks are more effective and accurate in many areas than traditional statistical models. The proposed study aims to predict prices on stock exchange via the hybrid artificial neural network models and metaheuristic algorithms which consist of cuckoo search, improved cuckoo search, improved cuckoo search genetic algorithm, genetic algorithm, and particle swarm optimization. The important 28 variables of value-added knowledge related to stock indices are identified as input parameters in this network, and then real values are obtained (http://www.tsetmc.com). The results of the proposed model suggest that particle swarm optimization is a dominant metaheuristic approach to predict stock price according to statistical performances of the above approaches.

A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization

Biologically-inspired algorithms are stochastic search methods that emulate the behavior of natural biological evolution to produce better solutions and have been widely used to solve engineering optimization problems. In this paper, a new hybrid algorithm is proposed based on the breeding behavior of cuckoos and evolutionary strategies of genetic algorithm by combining the advantages of genetic algorithm into the cuckoo search algorithm. The proposed hybrid cuckoo search-genetic algorithm (CSGA) is used for the optimization of hole-making operations in which a hole may require various tools to machine its final size. The main objective considered here is to minimize the total non-cutting time of the machining process, including the tool positioning time and the tool switching time. The performance of CSGA is verified through solving a set of benchmark problems taken from the literature. The amount of improvement obtained for different problem sizes are reported and compared with those by ant colony optimization, particle swarm optimization, immune based algorithm and cuckoo search algorithm. The results of the tests show that CSGA is superior to the compared algorithms.

A hybrid cuckoo search and genetic algorithm for reliability–redundancy allocation problems

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS–GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms’ performance. The computational results carried out on four classical reliability–redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS–GA algorithm proves to be extremely effective and efficient at locating optimal solutions.