Multiobjective optizition shuffled frog-leaping biclustering

Multistage distribution system expansion planning considering distributed generation using hybrid evolutionary algorithms

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.

A novel image watermark scheme based on the shuffled frog leaping (SFL) algorithm is proposed in the paper. The watermark is embedded to the discrete multi wavelet transform (DMT) coefficients larger than some threshold values, and watermark extraction is efficiently performed with SFL algorithm. Experimental results show both the robustness under attacks and the improvement in watermarked image quality with the proposed SFL 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.

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.

The shuffled frog leaping algorithm (SFLA) is presented, to optimally design multiple proportional–integral (PI) controllers of the static synchronous compensator (STATCOM) system with the purpose of improving the transient stability of a grid‐connected wind farm. The control strategy of the STATCOM system depends on a sinusoidal pulse width modulation voltage source converter, which is controlled by the cascaded PI control scheme. The response surface methodology is used to establish a second‐order fitted model of the transient voltage responses at the point of common coupling in terms of PI controllers’ parameters. For realistic responses, a three‐mass drive train model is used for the wind turbine generator system because of its great influence on the dynamic analyses. The SFLA code is built using MATLAB software. The effectiveness of optimised PI‐controlled STATCOM by the SFLA is then compared to that optimised by genetic algorithms technique taking into consideration symmetrical and unsymmetrical fault conditions. The proposed control scheme is applied to a real grid‐connected wind farm system at Hokkaido Island, Japan. The validity of the proposed system is verified by the simulation results, which are performed using PSCAD/EMTDC software environment. With the proposed SFLA‐based STATCOM, the transient stability of a grid‐connected wind farm can be enhanced.

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.

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

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.

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.

Shuffled frog-leaping algorithm (SFLA) is a recent addition to the family of stochastic search methods that mimic the social and natural behavior of species. SFLA combines the advantages of local search process of particle swarm optimization (PSO) and mixing of information of the shuffled complex evolution. The basic idea behind modeling of such algorithms is to achieve near to global solutions to the large-scale optimization problems and complex problems which cannot be solved using deterministic or traditional numerical techniques. In this study, the searching process is accelerated using golden section-based scaling factor and the constraints are handled by the penalty functions. Penalty functions are used to find the optimal solution for restrained optimization problems in the feasible region of the total search space. The resulting algorithm is named as Accelerated-SFLA. The proposal is implemented to solve the problem of optimal selection of processes. The results illustrate the efficacy of the proposal.

In a real life situation, due to the complexity of the industrial systems and non-linearity of their behavior, it is very difficult to achieve optimum performance of system for desired industrial goals using uncertain, vague and imprecise data. Herein, an approach has been proposed through which the behavior of the system is analyzed in the form of well- known six reliability indices by using triangular fuzzy numbers which allow the consideration of expert opinions, linguistic variables, operating conditions in reliability information. Using their behavior analysis a fuzzy multi-objective optimization problem (FMOOP) has been formulated. Due to the conflicting nature of the multiple objectives, the decision making is difficult and it leads to the Pareto optimal solutions instead of single optimal solutions. Many evolutionary algorithms (EAs) already exist in the literature for solving a multi-objective optimization problem (MOOP), and are termed as multi-objective evolutionary algorithms (MOEAs). Particle swarm optimization (PSO) is one of such MOEA which demonstrates the ability to identity a Pareto-optimal front efficiently. Here, a crisp optimization problem is reformulated from FMOOP by taking into account the preference of decision maker (DM) and then PSO is applied to solve the resulting fuzzified MOOP. The presented approach is applied in order to solve the multi-objective series-parallel system reliability optimization problem for a crystallization unit of a urea plant.

This study addresses a shuffled frog leaping algorithm for solving the multi-objective reactive power dispatch problem in a power system. Optimal Reactive Power Dispatch (ORPD) is formulated as a nonlinear, multi-modal and mixed-variable problem. The intended technique is based on the minimization of the real power loss, minimization of voltage deviation and maximization of the voltage stability margin. Generator voltages, capacitor banks and tap positions of tap changing transformers are used as optimization variables of this problem. A memetic meta-heuristic named as shuffled frog-leaping algorithm is intended to solve multi-objective optimal reactive power dispatch problems considering voltage stability margin and voltage deviation. The Shuffled Frog-Leaping Algorithm (SFLA) is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The most important benefit of this algorithm is higher speed of convergence to a better solution. The intended method is applied to ORPD problem on IEEE 57 bus power systems and compared with two versions of differential evolutionary algorithm. The simulation results show the effectiveness of the intended method.

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.