Beer froth artificial bee colony algorithm for job-shop scheduling problem

Sharma, Nirmala; Sharma, Harish; Sharma, Ajay

doi:10.1016/j.asoc.2018.04.001

Cited by 69 publications

(23 citation statements)

References 41 publications

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“…Panda and Swamy [31] used a new ABC to find the optimal scenarios in pavement resurfacing optimization problem. Sharma et al [32] designed a new ABC based on beer froth decay phenomenon to find the optimal job sequence in job shop scheduling problem.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Zhou

Wang

2019

Mathematical Problems in Engineering

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To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Zhou

Wang

2019

Mathematical Problems in Engineering

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“…La ecuación (9) describe que un trabajador no puede ejecutar una tarea sino tiene la habilidad para hacerlo. La ecuación (10) asegura que se asigne una tarea a un operador. Las ecuaciones (11) a (12) aseguran el dominio de las variables.…”

Section: Función Objetivounclassified

“…En otro trabajo se adaptó una metaheurística basada en la migración e inmigración de especies entre hábitat llamada Biogeography-Based Optimization (BBO) con el fin de minimizar el Makespan [9]. Una nueva adaptación del algoritmo colonia de abejas o ABC fue utilizado para mantener un balance entre las fases de exploración y explotación, e incluye el fenómeno de la espuma de la cerveza como método de generación de nuevas posiciones [10]. La adaptación se realizó para un problema Job Shop Clásico con el fin de minimizar el Makespan.…”

Section: Introduccionunclassified

Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

2019

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Introducción: El problema Job Shop Con Operadores Calificados o Job Shop With Skilled Operators (JSSO) es una extensión del problema clásico Job Shop en el cual, una operación debe ser ejecutada por un conjunto limitados de trabajadores, con el objetivo de minimizar el tiempo de terminación total de los trabajos o Makespan, situación que puede representar distintas aplicaciones en la vida cotidiana. Es un problema complejo y es catalogado como NP-HARD. Objetivo: En este artículo, se aborda el problema JSSO desde la adaptación de un algoritmo conocido como Squirrel Search Algorithm (SSA). Metodología: Se propone un esquema de codificación discreto para el algoritmo SSA utilizando el método Smallest Position Value (SPV). Además, para evitar soluciones que violen las relaciones de precedencia; se corrigen con el método Valid Particle Generator (VPG), el cual garantiza soluciones factibles. Dos versiones del algoritmo se colocan a prueba en 28 instancias propuesta en la literatura para validar su rendimiento. Resultados: Los experimentos computacionales realizados muestran que los dos algoritmos propuestos alcanzan soluciones óptimas en 25 de las 28 instancias analizadas. Además, para las instancias en donde no se logró soluciones óptimas, el gap promedio no supera el 2% para ambas versiones de los algoritmos propuestos. Conclusiones: El esquema de codificación propuesto garantiza la discretización del algoritmo, generando soluciones que convergen hacia el óptimo. Además, la codificación propuesta, permite utilizar de manera natural los operadores de movimiento propuestos originalmente para el algoritmo utilizado. El rendimiento obtenido por los algoritmos es adecuado y de alta calidad.

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“…The DE to enhance the local optimization capability of the algorithm and to keep the diversity of the scheduling scheme to prevent the algorithm from premature convergence. Nirmala Sharma et al [32] improved the iterative formula for onlooker bees in ABC to solve JSSP. Xueni Qiu et al [33] combined artificial immune system (AIS) theory and PSO to solve the JSSP.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

Gao

Wang

et al. 2020

Mathematics

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Job shop scheduling problem (JSSP) has high theoretical and practical significance in academia and manufacturing respectively. Therefore, scholars in many different fields have been attracted to study this problem, and many meta-heuristic algorithms have been proposed to solve this problem. As a meta-heuristic algorithm, particle swarm optimization (PSO) has been used to optimize many practical problems in industrial manufacturing. This paper proposes a hybrid PSO enhanced with nonlinear inertia weight and and Gaussian mutation (NGPSO) to solve JSSP. Nonlinear inertia weight improves local search capabilities of PSO, while Gaussian mutation strategy improves the global search ability of NGPSO, which is beneficial to the population to maintain diversity and reduce probability of the algorithm falling into the local optimal solution. The proposed NGPSO algorithm is implemented to solve 62 benchmark instances of JSSP, and the experimental results are compared with other algorithms. The results obtained by analyzing the experimental data show that the algorithm is better than other comparison algorithms in solving JSSP.

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Beer froth artificial bee colony algorithm for job-shop scheduling problem

Cited by 69 publications

References 41 publications

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Improving Artificial Bee Colony Algorithm Using a Dynamic Reduction Strategy for Dimension Perturbation

Un Squirrel Search Algorithm discreto aplicado al problema Job Shop con operadores calificados

A Hybrid Particle Swarm Optimization Algorithm Enhanced with Nonlinear Inertial Weight and Gaussian Mutation for Job Shop Scheduling Problems

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