The Job Shop Scheduling Problem (JSSP) is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, we develop a three-stage hybrid approach called JSFMA to solve the JSSP. In JSFMA, considering a method similar to Shuffled Frog Leaping algorithm we divide the population in several sub populations an...

In this paper a novel method based on evolutionary algorithms is presented to estimate the harmonic components. In general, the optimization of the harmonic estimation process is a multi-component problem, in which evaluation of the phase and harmonic frequency is the nonlinear part of the problem and is solved based on the mathematical and evolutionary methods; while estimation of amplitude of...

 In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification....

In manufacturing systems, generally employee timetabling and job shop scheduling is considered to be too hard and complex. To achieve an integrated manufacturing environment, the integration of employee timetabling and job shop scheduling is essential. It is a process of assigning employees into work slots in a pattern according to the hierarchy level of the employee, job allotment, individual ...

This paper deals with the design of multi-machine power system stabilizers (PSSs) and Static var compensator (SVC) using Modified shuffled frog leaping algorithm (MSFLA). The effectiveness of the proposed scheme for optimal setting of the PSSs and SVC controllers has been attended. The PSSs and SVC controllers designing is converted to an optimization problem in which the speed deviations betwe...

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The ...