Solving the parameter identification problem using shuffled frog leaping with opposition-based initialization (original) (raw)
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An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation
Information Sciences, 2012
Several types of evolutionary computing methods are documented in the literature and are well known for solving unconstrained optimisation problems. This paper proposes a hybrid scheme that combines the merits of a global search algorithm, the shuffled frog-leaping algorithm (SFLA) and local exploration, extremal optimisation (EO) and that exhibits strong robustness and fast convergence for high-dimensional continuous function optimisation. A modified shuffled frog-leaping algorithm (MSFLA) is investigated that improves the leaping rule by properly extending the leaping step size and adding a leaping inertia component to account for social behaviour. To further improve the local search ability of MSFLA and speed up convergence, we occasionally introduce EO, which has an excellent local exploration capability, in the local exploration process of the MSFLA. It is characterised by alternating the coarse-grained Cauchy mutation and the fine-grained Gaussian mutation. Compared with standard particle swarm optimisation (PSO), SFLA and MSFLA for six widely used benchmark examples, the hybrid MSFLA-EO is shown to be a good and robust choice for solving high-dimensional continuous function optimisation problems. It possesses excellent performance in terms of the mean function values, the success rate and the fitness function evaluations (FFE), which is a rough measure of the complexity of the algorithm.
Current Studies and Applications of Shuffled Frog Leaping Algorithm: A Review
Archives of Computational Methods in Engineering, 2022
Shuffled Frog Leaping Algorithm (SFLA) is one of the most widespread algorithms. It was developed by Eusuff and Lansey in 2006. SFLA is a population-based metaheuristic algorithm that combines the benefits of memetics with particle swarm optimization. It has been used in various areas, especially in engineering problems due to its implementation easiness and limited variables. Many improvements have been made to the algorithm to alleviate its drawbacks, whether they were achieved through modifications or hybridizations with other well-known algorithms. This paper reviews the most relevant works on this algorithm. An overview of the SFLA is first conducted, followed by the algorithm's most recent modifications and hybridizations. Next, recent applications of the algorithm are discussed. Then, an operational framework of SLFA and its variants is proposed to analyze their uses on different cohorts of applications. Finally, future improvements to the algorithm are suggested. The main incentive to conduct this survey to provide useful information about the SFLA to researchers interested in working on the algorithm's enhancement or application.
Improved Shuffled Frog Leaping Algorithm
Shuffled frog leaping algorithm is a memetic metaheuristic and population based intelligent inquiry metaphor im-pacted by normal memetics. Predominantly SFLA has been utilized for arrangement of combinative streamlining issues. In SFL algorithm there are two basic things one is population which is divided into several memeplexes and another is information between these memeplexes has been exchanged. In Shuffled frog leaping algorithm one issue is available which is slow convergence. To resolve this problem Improved shuffled frog leaping algorithm is proposed. In both phase local best and global best phase (1-it/Maxit) term is multiplied to get better solution. The proposed strategy is tested more than 12 benchmark functions and compared with different algorithms like basic shuffled frog leaping algorithm (SFLA), gravitational search algorithm (GSA), spider monkey optimization (SMO) and differential evolution (DE).
An Efficient Modified Shuffled Frog Leaping Optimization Algorithm
2011
In this paper, a modified shuffled frog leaping (MSFL) algorithm is proposed to overcome drawbacks of standard shuffled frog leaping (SFL) method. The MSFL approach is based on two major modifications on the conventional SFL method: (1) an adaptive accelerated position changing of frogs and (2) sweeping between randomly selected frogs (called superseding frogs). The first modification causes a fast convergence rate and consequently achieving a rapid adaptive algorithm, while the second one causes a better diversification and consequently escaping from local optimum traps. The MSFL algorithm performance is validated using benchmark functions. Simulation results indicate the superiority of MSFL to that of the original SFL in terms of optimal precision and fast convergence rate.
Bespoke Shuffled Frog Leaping Algorithm and its Engineering Applications
International Journal of Intelligent Systems and Applications, 2015
Shuffled Frog Leap Algorithm (SFLA), a metaheuristic algorithms inspired by PSO and DE has proved its efficacy in solving discrete optimization problems. In this paper we have modified SFLA to solve constrained engineering design problems. The proposed modification integrates a simple mechanism to update the position of frog in its memeplex in order to accelerate the basic SFLA algorithm. The proposal is validated on four engineering design problems and the statistical results are compared with the state-of-art algorithms. The simulated statistical results indicate that our proposal is a promising alternative to solve these types of optimization problems in terms of convergence speed.
Improved shuffled frog leaping algorithm for continuous optimisation adapted SEVO toolbox
International Journal of Advanced Intelligence Paradigms, 2013
This paper presents improved shuffled frog leaping algorithm (ISFLA) with controlled random search behaviour. The work proposes adaptation of random solution generation rule with control parameter to manage the direction of search in conventional SFLA. To evaluate the effectiveness of ISFLA, it has been compared with respect to GA, MA, PSO and SFLA for large dimensions-100, 500 and 1,000 over benchmark test problems using SEVO toolbox. Results depict that ISFLA performs considerably better for all benchmark problems. Results also demonstrated the utility and simplicity of SEVO toolbox for simulating new algorithms. ANOVA test substantiated the statistical significance of the obtained results.
International Journal of Applied Evolutionary Computation, 2014
In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i.e. Nelder–Mead simplex search method and bidirectional random optimization with two meta-heuristic methods i.e. the shuffled frog leaping and the shuffled complex evolution, respectively. In this hybrid methodology, each subset of meta-heuristic algorithms is improved by a hybrid strategy that is combined from evolutionary process of each subset in related algorithm and a local search method. These hybrid algorithms are evaluated on low and high dimensional continuous benchmark functions and the obtained results are compared with their non-hybrid competitors. The obtained results demonstrate that the hybrid algorithm combined from shuffled frog leaping and Nelder–Mead simplex has a better success rate but a higher number of function evaluations on low-dimensional functions than the shuffled frog leaping. Whereas on high-dimensional functions it has a better succe...
Differential evolution using quadratic interpolation for initializing the population
Advance Computing Conference, …, 2009
The performance of population based search techniques like Differential Evolution (DE) depends largely on the selection of initial population. A good initialization scheme not only helps in giving a better final solution but also helps in improving the convergence rate of the algorithm. In the present study we propose a novel initialization scheme which uses the concept of quadratic interpolation to generate the initial population. The proposed DE is validated on a test bed of 10 benchmark problems with varying dimensions and the results are compared with the classical DE using random initialization, DE using opposition based learning for generating the initial population. The numerical results show that the proposed algorithm using quadratic interpolation for generating the initial population accelerates the convergence speed quite considerably.
A comparative study of differential evolution algorithms for parameter fitting procedures
2016 IEEE Congress on Evolutionary Computation (CEC), 2016
Parameter fitting consists on the estimation of model parameters using experimental data from the studied process, which can be considered as a nonlinear optimization problem. In this sense, evolutionary computation has shown its great capability to solve multimodal nonlinear optimization problems. This paper compares different variants of the Differential Evolution (DE) algorithm to minimize the residual sum of squares between the outcome of the mathematical model and experimental data. To compare the different variants of the DE algorithm, a biopolymer production model is considered. Simulations results suggest a trend for the best fit using the DE/best/ variants. However, the DE/rand/ variant provides more stable results respect to the average and standard deviation of different trials. Finally, the biopolymer production problem is discussed.