A Bat Algorithm with Generalized Walk for the Two-Stage Hybrid Flow Shop Problem

Abstract

In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of Bat Algorithm (BA) with Generalized Evolutionary Walk Algorithm (GEWA) to solve the mono-processors two stages Hybrid Flow Shop scheduling. The authors compare the modified bat algorithm with the original one, with Particle Swarm Optimization (PSO) and with others results taken from literature. Computational results on a standard two-stage hybrid flow shop benchmark of 70 cases, and about 1700 instances, indicate that the proposed algorithm finds the best makespan (Cmax) in a good processing time comparing to the original bat algorithm and other algorithms.

Multi-Objective Electrical Power System Design Optimization Using a Modified Bat Algorithm

“…A bat algorithm with generalized flight (BAG) is a hybridization of the bat algorithm and the GEWA algorithm. It was first introduced by [37] and then applied to manufacturing system scheduling in [38], and to a green economic power dispatch problem in [39]. Our improvement lies in adding a global search function to the classical BA, which is the global flight of the worst bats.…”

“…GEWA is an algorithm based on a generic optimization principle [53], which has been investigated in [37,41,54]. It is based on a random global search that replaces the poor positions of a population of walkers.…”

“…The authors of [37] improved the standard bat algorithm with a global flight of the worst bat, as with the principle of the generalized evolutionary walker algorithm. The wrong bat, and eventually many bats, exclusively flyaccording to a global search function following Equations ( 21) and (23).…”

Multiple Resource Surgical Case Scheduling Problem: Ant Colony System Approach

“…NP-hard problems in large-scale are tackled by meta-heuristic and swarm intelligence algorithms (Dekhici andBelkadi, 2015, Arun andKumar, 2017). In many researches in the field of the SCS problem, some heuristic or meta-heuristic procedures such as genetic algorithm (Marques et al, 2014), simulated annealing Demeulemeester, 2007, Beliën et al, 2009), tabu search (Lamiri et al, 2009, Saremi et al, 2013, and ant colony optimization (Xiang et al, 2015 were developed to achieve near-optimal solutions, because this problem is NP-hard combinatorial optimization problem (Marques et al, 2014).…”

“…Constraints (8) and (9) specify that two different operations of Oij and Ohg cannot be processed at the same time on any resource in set Rij∩Rhg. Equation 10ensures that jth operation of the patient must be exactly started after the end time of (j-1)th of the operation of the same patient.…”