Accelerated Shuffled Frog-Leaping Algorithm

PurposeAttitude control of unmanned aerial vehicle (UAV) is the purposeful manipulation of controllable external forces to establish a desired attitude, which is inner‐loop of the autonomous flight control system. In the practical applications, classical control methods such as proportional‐integral‐derivative control are usually selected because of simple and high reliability. However, it is usually difficult to select or optimize the control parameters. The purpose of this paper is to investigate an intelligent algorithm based classical controller of UAV.Design/methodology/approachAmong the many intelligent algorithms, shuffled frog leaping algorithm (SFLA) combines the benefits of the genetic‐based memetic algorithm as well as social behavior based particle swarm optimization. SFLA is a population based meta‐heuristic intelligent optimization method inspired by natural memetics. In order to improve the performance of SFLA, a different dividing method of the memeplexes is presented to make their performance balance; moreover, an evolution mechanism of the best frog is introduced to make the algorithm jump out the local optimum. The modified SFLA is applied to the tuning of the proportional coefficients of pitching and rolling channels of UAV flight control system.FindingsSimulation of a UAV control system in which the nonlinear model is obtained by the wind tunnel experiment show the rapid dynamic response and high control precision by using the modified SFLA optimized attitude controller, which is better than that of the original SFLA and particle swarm optimization method.Originality/valueA modification scheme is presented to improve the global searching capability of SFLA. The modified SFLA based intelligent determination method of the UAV flight controller parameters is proposed, in order to improve the attitude control performance of UAV.

Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

Several evolutionary algorithms (EAs) are proposed in the literature to solve continuous optimisation problems. In this paper we present a new search startegy to improve the efficiency of the Shuffled Frog Leaping Algorithm (SFLA). The shuffled frog leaping algorithm is a population-based approach for a heuristic search in optimization problems. The algorithm consists of a set of virtual frogs partitioned into several groups called “memeplexes”. However, After some optimization runs frogs position's become closer in each memeplex. Indeed, this problem leads to a premature convergence. For getting better effiency we propose a novel search strategy by infecting not only the worst indvidual but also the best indvidual idea's. To further improve the speed of convergence of the algorithm, we have introduced two acceleration factors in the search strategy formulation. The proposed algorithm has been evaluated on five mathematical benchmark functions. Compared with a the orginal SFLA and the particle swarm optimization algorithm, the experimental results in terms of optimization performance and the speed of convergence shows that the proposed algorithm can be an effective tool for solving combinatorial optimization problems.

Chapter 10 - Constraint-based heuristic algorithms for software test generation

Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithmic equivalence of particle swarm optimization (PSO) and various other newer SI algorithms, including the shuffled frog leaping algorithm (SFLA), the group search optimizer (GSO), the firefly algorithm (FA), artificial bee colony algorithm (ABC) and the gravitational search algorithm (GSA). We find that the original versions of SFLA, GSO, FA, ABC, and GSA, are all algorithmically identical to PSO under certain conditions. We discuss their diverse biological motivations and algorithmic details as typically implemented, and show how their differences enhance the diversity of SI research and application. Then we numerically compare SFLA, GSO, FA, ABC, and GSA, with basic and advanced versions on some continuous benchmark functions and combinatorial knapsack problems. Empirical results show that an advanced version of ABC performs best on the continuous benchmark functions, and advanced versions of SFLA and GSA perform best on the combinatorial knapsack problems. We conclude that although these SI algorithms are conceptually equivalent, their implementation details result in notably different performance levels.

Biclustering of DNA microarray data that can mine significant patterns to help in understanding gene regulation and interactions. This is a classical multi-objective optimization problem (MOP). Recently, many researchers have developed stochastic search methods that mimic the efficient behavior of species such as ants, bees, birds and frogs, as a means to seek faster and more robust solutions to complex optimization problems. The particle swarm optimization(PSO) is a heuristics-based optimization approach simulating the movements of a bird flock finding food. The shuffled frog leaping algorithm (SFLA) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. This paper introduces SFL algorithm to solve biclustering of microarray data, and proposes a novel multi-objective shuffled frog leaping biclustering(MOSFLB) algorithm to mine coherent patterns from microarray data. Experimental results on two real datasets show that our approach can effectively find significant biclusters of high quality.

Gear drives are extremely critical elements of transmission systems. Designing gears that satisfy the application's requirements like load carrying capacity and strength whilst also being compact, lightweight, and economical is quite a challenging task. In recent times, a lot of research has been dedicated to optimizing speed reducers by making use of metaheuristic nature-inspired algorithms. The present work optimizes the weight of a spur speed reducer by employing two modified nature-inspired algorithms: Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). Parameter optimization was carried out to select the best combination of c1, c2 and 𝜔 for the selected case study. It was found that there was a 1.1619% and 13.41% decrease in the cost function evaluation, as compared to the Crude Monte-Carlo and Stray Process methods used in the original case study, and around an 11% decrease as compared to results in published literature.

The accuracy of PV array model directly affects the output characteristics of the PV system. In this paper, a simplified engineering model for PV array is established and the influence on the output characteristics of PV array is analyzed when Rs, Rsh and α, β, γ change. According to the actual data of a PV power station, the shuffled frog leaping algorithm (SFLA) is adopted to identify the parameters in the model. The results show that the identification curve and the measured curve are in a better consistency. Then, the particle swarm optimization (PSO) algorithm is adopted to identify the same parameters and the identification results are compared with the identification results of SFLA. It shows that the output of PV array model based on the identification results of SFLA has a higher fitting degree with the actual output of PV power station which further verifies the superiority of SFLA.

In this paper, the Authors present the designing of power system stabilizer (PSS) and static var compensator (SVC) based on chaos, particle swarm optimization (PSO) and shuffled frog leaping (SFL) Algorithms has been presented to improve the power system stability. Single machine infinite bus (SMIB) system with SVC located at the terminal of generator has been considered to evaluate the proposed SVC and PSS controllers. The coefficients of PSS and SVC controller have been optimized by Chaos, PSO and SFL algorithms. Fi-nally the system with proposed controllers is simulated for the special disturbance in input power of genera-tor, and then the dynamic responses of generator have been presented. The simulation results show that the system composed with recommended controller has outstanding operation in fast damping of oscillations of power system and describes an application of Chaos, PSO and SFL algorithms to the problem of designing a Lead-Lag controller used in PSS and SVC in power system.

This study presents a novel approach for the modelling of transformer core magnetisation characteristics under DC bias condition by using the inverse Jiles–Atherton (J–A) model. An improved shuffled frog leaping algorithm (SFLA) is proposed to identify the five parameters of J–A model, where an adaptive chaotic mutation operation is added in the global searching process to increase the identification accuracy. With the proposed algorithm, the J–A model parameters under different DC components are identified based on the DC‐bias experiment on the real transformer. The conventional SFLA and particle swarm optimisation (PSO) method are also applied to identify the parameters of J–A model. All the identified results are compared with the measured B–H curves to verify their identification accuracy. Moreover, the least square support vector machine (LSSVM) algorithm is used to predict the J–A model parameters of transformer under larger DC component from the previously identified parameters in smaller DC. The calculated results have shown that the improved SFLA has higher identification accuracy than the conventional SFLA and PSO methods. Furthermore, LSSVM algorithm can effectively forecast the transformer magnetisation character under large DC bias condition, which is beneficial for the research of transformer DC bias problem.

This paper addresses stochastic transmission expansion planning (TEP) under uncertain load conditions when reliability is taken into consideration. The main objective of the proposed TEP is to minimize the total planning cost by denoting the place, number, and type of new transmission lines subject to safe operation criteria. In this paper, the objective function consists of two terms, namely, investment cost (IC) of new lines and reliability cost. The reliability cost is incorporated as the loss of load cost (LOLC). Network uncertainties in the form of loads are molded as Gaussian probability distribution function (PDF). Monte-Carlo simulation is applied to tackle the uncertainties. The proposed stochastic TEP is expressed as constrained optimization planning and solved using shuffled frog leaping algorithm (SFLA) SFLA is compared to other optimization techniques such as particle swarm optimization (PSO) and genetic algorithms (GA). Finally, stochastic planning (planning including uncertainty) and deterministic planning (planning excluding uncertainty) are compared to demonstrate impacts of uncertainty on the results. Simulation results in different cases and scenarios verify the effectiveness and viability of the proposed stochastic TEP, including uncertainty and reliability.

Centroid Mutation Embedded Shuffled Frog-Leaping Algorithm

There are numerous approaches being used to reduce power losses in the distribution system. In this work, the concept of network reconfiguration is used in an effort to provide new algorithms to decrease distribution system losses. The network reconfiguration issue must be solved with effective distribution load flow (DLF). Two key components are required for the Optimal Network Reconfiguration (ONR) strategy to reduce losses: first, promising radiality for the reconfigured network, and second, providing optimal losses for the final reconfigured network. The complex computational problem of network reconfiguration is addressed using five meta-heuristic techniques: Genetic Algorithm (GA), Shuffled Frog Leap Algorithm (SFLA), Particle Swarm Optimization (PSO), Pigeon Inspired Optimization (PIO), and Jaya Optimization algorithm. These techniques take into account equality and inequality constraints to find the best network reconfiguration with the minimum power losses in the system. The IEEE 33 bus distribution system is used as a test case, and the test system is subjected to network reconfiguration. Results of different algorithms were included. Over the other algorithms, the Pigeon Inspired Optimization (PIO) algorithm outperforms them.

Today using evolutionary programing for solving complex, nonlinear mathematical problems like optimum power flow is commonly in use. These types of problems are naturally nonlinear and the conventional mathematical methods aren’t powerful enough for achieving the desirable results. In this study an Optimum Power Flow problem solved by means of minimization of fuel costs for IEEE 30 buses test system by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO) and Shuffle Frog Leaping Algorithm (SFLA), these algorithms has been used in MATLAB medium with help of MATHPOWER to achieving more precise results and comparing these results with the other proposed results in other published papers.