Replica creation strategy based on quantum evolutionary algorithm in data gird

Each university has different regulations in making lecture schedule according to the conditions of respective institution. In this study, regulation on lecture scheduling used the maximum limit of credits of lecturer in a day, in which if exceeds the maximum limit, then the schedule of course will be moved on another day. This study attempts to optimize the schedule preparation in each semester by considering the maximum limit of credits of a lecturer in a day. Lecture scheduling is a combination of space, time, and resources. It is categorized into combinatorial optimization group. There are two methods for solving the combinatorial optimization problems, i.e. exact and approximation method. The approximation method consists of two types, heuristics and metaheuristics. The algorithm metaheuristics categories include genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), bee colony optimization (BCO), simulated annealing, and so on. This paper employed metaheuristics approach. Research on the preparation of lecture scheduling using ACO algorithm has been conducted and proven to be able to prepare the lecture scheduling. The ACO algorithm has numbers of parameters that in solving issues, one must manually set a number of parameters by using the Design of Experiments (DoE) tool, which takes time to solve the optimization problem. Hence, to solve the issue, an automated parameter optimization is required. PSO algorithm has fewer parameters compared to the ACO. Therefore, the purpose of this paper is a hybrid between PSO and ACO in preparing lecture scheduling.

Quantum Evolutionary Algorithm (QEA) is an optimization algorithm based on the concept of quantum computing and Particle Swarm Optimization (PSO) algorithm is a population based intelligent search technique. Both these techniques have good performance to solve optimization problems. PSEQEA combines the PSO with QEA to improve the performance of QEA and it can solve single objective optimization problem efficiently and effectively. In this paper, PSEQEA is studied to solve multi-objective Optimization (MO) problems. Some well-known non-trivial functions are used to observe the performance of PSEQEA to detect the Pareto optimal points and the shape of the Pareto front using both Fixed Weighted Aggregation method and Adaptive Weighted Aggregation method. Moreover, Vector Evaluated PSEQEA (VEPSEQEA) borrows concept from Schaffer's Vector Evaluated Genetic Algorithm (VEGA) that can also cope with MO problems. Simulation results show that PSEQEA and VEPSEQEA perform better than PSO and VEPSO to discover the Pareto frontier.

Genetic Algorithms (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colonies (ABC) are some vital nature inspired computing (NIC) techniques. These approaches have been used in early prophecy of various diseases. This article analyzes the efficacy of various NIC techniques in diagnosing diverse critical human disorders. It is observed that GA, ACO, PSO and ABC have been successfully used in early diagnosis of different diseases. As compared to ACO, PSO and ABC algorithms, GA has been extensively used in diagnosis of ecology, cardiology and endocrinologist. In addition, from the last six years of research, it has been observed that the accuracy accomplished using GA, ACO, PSO and ABC in the early diagnosis of cancer, diabetes and cardio problems lies between 73.5%-99.7%, 70%-99.2%, 80%-98% and 76.4% to 99.98% respectively. Furthermore, ACO, PSO and ABC are found to be best suited in diagnosing lung, prostate and breast cancer respectively. Moreover, the hybrid use of NIC techniques produces better results as compared to their individual use.

Fuzzy systems (FSs) have been extensively applied to automatic control, pattern recognition, and decision analysis. However, a common bottleneck is encountered in the derivation of fuzzy rules, which is often difficult, time consuming, and relies on expert knowledge. To automate the design of FSs, many metaheuristic learning algorithms have been proposed. One major optimization category uses Swarm Intelligence (SI) model (Kennedy et al., 2001). The SI technique studies collective behavior in decentralized systems. Its development was based on mimicking the social behavior of animals or insects in an effort to find the optima in the problem space. SI models are initialized with a population of random solutions. One well-known SI model is particle swarm optimization (PSO) (Kennedy & Eberhart, 1995). Many modified PSO models have been proposed and successfully applied to different optimization problems (Clerc & Kennedy, 2002; Bergh & Engelbrecht, 2004; Ratnaweera et al., 2004; Juang, 2004; Kennedy & Mendes, 2006; Parrott & Li, 2006; Chen & Li, 2007). FS design using PSO has also been proposed in several studies (Juang, 2004; Chatterjee et al., 2005; Juang et al., 2007; Araujo & Coelho, 2008; Sharma et al., 2009). Another well-known SI is ant colony optimization (ACO) (Dorigo & Stutzle, 2004). The ACO technique is inspired by real ant colony observations. It is a multi-agent approach that was originally proposed to solve difficult discrete combinatorial optimization problems, such as the traveling salesman problem (TSP) (Dorigo et al., 1996; Dorigo & Gambardella, 1997). In the original ACO meta-heuristic, artificial ant colonies cooperate to find good solutions for difficult discrete optimization problems. Different ACO models have been applied to FS design problems (Cassillas et al., 2000; Cassillas et al., 2005; Mucientes & Casillas; 2007; Juang & Lo, 2007; Juang et al., 2008; Juang & Lu; 2009). In (Cassillas et al.,2000; Mucientes & Casillas; 2007; Juang et al., 2008; Juang & Lu; 2009), the FS input space was partitioned in grid type with antecedent part parameters of an FS manually assigned in advance. In (Juang & Lo, 2007), the FS input space was flexibly partitioned using a fuzzy clustering-like algorithm in order to reduce the total number of rules. For all of these studies, the consequent part parameters were optimized in discrete space using ACO. Since only the consequent part parameters are optimized, and the optimization space is restricted to be discrete, the designed FSs are unsuitable for problems where high accuracy is a major concern. Source: Fuzzy Systems, Book edited by: Ahmad Taher Azar, ISBN 978-953-7619-92-3, pp. 216, February 2010, INTECH, Croatia, downloaded from SCIYO.COM

Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution, to get ant colony algorithm's initial pheromone distribution and according to the pheromone content to eliminate some poor path. Secondly, using ant colony algorithm for the optimal path, it was gotten by particle swarm optimization for the secondary search. Finally, get the optimal solution of path search. The simulation results show that, the fusion algorithm does better than the ant colony algorithm in terms of path optimization.

In this chapter, one gives an introduction to different kinds of particle swarm optimization (PSO) algorithms. One also introduces artificial neural networks (ANNs), support vector machines (SVMs) and evolutionary computing to show how PSO may be used to determine optimal parameters using an ANN or SVM regime, for classification of DNA strings. In addition, PSO is used in the design of an SVM-based clustering algorithm. Ant colony optimization (ACO) algorithms are also introduced in the chapter. Using ACO algorithms has been of great success to solve many discrete optimization and non-deterministic polynomial (NP)-hard problems, for instance the travelling salesman problem. The behaviour of ants is also used, for instance, to design an algorithm for data clustering. We want to develop later a similar application based on this clustering algorithm and compare it with the SVM one using PSO.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Since the era of big data, enhancement of the speed of the database query is the critical problem. Fundamental part of any database management system is query optimizer. So, it should be able to choose the suitable search strategy which can give quicker response even with complex and large amount of data. The expansion of database application areas, where a query may include more than 100 joins, traditional query optimization techniques, i.e., greedy-based optimization and dynamic programming unable to find the solution but its observed from the metaheuristic algorithm like genetic algorithms (GA), particle swarm optimization (PSO) and ant colony optimization (ACO) algorithms gives optimal solution. These algorithms do not guarantee the best optimal solution, rather near best solution, which is tolerable. However, there is the limitation of slow convergence and high computation effort in GA and stagnation with premature convergence in ACO. Therefore, we proposed the hybridization of our modified ACO with GA to overcome the above limitations for multi-join query optimization and experimentally shown our proposed method on randomly created dataset gives better results over GA and ACO in terms of distance and execution times.

Two-echelon logistics distribution region partitioning problem based on a hybrid particle swarm optimization–genetic algorithm

The rational planning of land around rail transit stations in cities can effectively improve the convenience of transportation and economic development of cities. This paper briefly introduced the transit-oriented development (TOD) mode of urban planning. We constructed a hierarchical structure for evaluating the quality of land plan-ning of urban rail transit stations through the analytic hierarchy process (AHP) method. The structure started from three large aspects, i.e., traffic volume, regional environmental quality, and regional economic efficiency, and every large aspect was divided into three small aspects. Then, an optimization model was established for land planning of rail transit stations. The land planning scheme was optimized by a genetic algorithm (GA). To enhance the optimization performance of the GA, it was improved by coevolution, i.e., plural populations iterated inde-pendently, and every population replaced the poor chromosomes in the other populations with its excellent chro-mosomes in the previous process. Finally, the Jinzhonghe street station in Hebei District, Tianjin city, was taken as a subject for analysis. The results suggested that the improved GA obtained a set of non-inferior Pareto solutions when solving a multi-objective optimization problem. The distribution of solutions in the set also indicated that any two objectives among traffic volume, environmental quality, and economic efficiency was improved at the cost of the remaining objectives. The land planning schemes optimized by the particle swarm optimization (PSO) algo-rithm, the traditional GA, and the improved GA, respectively, were superior than the initial scheme, and the opti-mized scheme of the improved GA was more in line with the characteristics of the TOD mode than the traditional one and the PSO algorithm, and the fitness value was also higher. In conclusion, the GA can be used to optimize the planning design of land in rail transit areas under the TOD mode, and the optimization performance of the GA can be improved by means of coevolution.

To determine the optimal trajectory of mobile robots in a complex environment and to enhance the effectiveness of search efficiency and path optimization, an improved ant colony (ACO) algorithm is proposed. A fusion algorithm of improved particle swarm optimization (PSO) and ACO algorithm is proposed to solve for optimal paths. Firstly, we improve the PSO and adaptively adjust the inertia weight to facilitate the particle search. Besides, we disturb the particles with the chaotic variables to increase the convergence speed. Secondly, the pheromone distribution on the path is adjusted based on the optimal solution of PSO to solve the initial pheromone insufficiency issue in the ACO algorithm. Then, to optimize the update strategy, we adaptively adjust the pheromone intensity values. We also provide a wide range of control schemes for updating the parameters for balancing the search capability and convergence. The results in the simulation environment show that our algorithm is more effective than other improved algorithms. The improved fusion algorithm converges faster and finds shorter optimal path. The method can improve the comprehensive performance and achieve the rapid path planning for mobile robots.

Expanding network lifespan is the main target during the design of a wireless sensor network. Clustering the sensor nodes is an efficient topology to accomplish this objective. In this work, we offer a reactive hybrid protocol to enhance network lifetime using the hybridization of ant colony optimization (ACO) along with particle swarm optimization (PSO) algorithm. In order to improve the energy efficiency, the anticipated RAP algorithm uses a reactive data transmission strategy which is incorporated into the hybridization of ACO and PSO algorithm. In the beginning, the clusters are organized depending on the residual energy and then the proposed RAP algorithm will be executed to improvise the inter-cluster data aggregation and reduces the data transmission. The experimental outcomes demonstrate the proposed RAP algorithm performs well against other near conventions in different situations.

The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO) algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO) algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO) algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

The objective of the research work is to examine energy-efficient routing in wireless sensor networks for moving nodes by contrasting new evolutionary algorithms with the Particle Swarm Optimization (PSO) Algorithm. Comparative analysis of energy efficient routing is performed by a novel genetic algorithm where there are several samples (N = 10) and a PSO algorithm where there are several samples (N = 10) techniques with pre-test power of 80% using NS2. When routing wireless sensor networks with moving nodes, Various metrics, including delay, energy use, and packet delivery ratio, are used to compare PSO and genetic algorithms. The evolutionary algorithm outperforms the PSO algorithm in these ways in terms of packet delivery ratio, energy usage, and delay. (p < 0.05) indicates a statistically significant difference. The research's findings comprised the three metrics of delay, energy consumption and packet delivery ratio. The method shows that the novel Genetic algorithm outperforms the PSO algorithm for wireless sensor network energy-efficient routing for moving nodes by identifying a set of routes that can satisfy the delay restrictions.KeywordsNovel genetic algorithmParticle Swarm Optimization (PSO)Wireless sensor networkDelayEnergy consumptionPacket delivery ratio