An Improved Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem DOI Creative Commons
Alfonsas Misevičius,

Aleksandras Andrejevas,

Armantas Ostreika

и другие.

Mathematics, Год журнала: 2024, Номер 12(23), С. 3726 - 3726

Опубликована: Ноя. 27, 2024

In this paper, an improved hybrid genetic-hierarchical algorithm for the solution of quadratic assignment problem (QAP) is presented. The based on genetic search combined with hierarchical (hierarchicity-based multi-level) iterated tabu procedure. following are two main scientific contributions paper: (i) enhanced two-level primary (master)-secondary (slave) proposed; (ii) augmented universalized multi-strategy perturbation (mutation process)—which integrated within a multi-level algorithm—is implemented. proposed scheme enables efficient balance between intensification and diversification in process. computational experiments have been conducted using QAP instances sizes up to 729. results from demonstrate outstanding performance new approach. This especially obvious small- medium-sized instances. Nearly 90% runs resulted (pseudo-)optimal solutions. Three best-known solutions achieved very hard, challenging

Язык: Английский

A simple migrating birds optimization algorithm with two search modes to solve the no-wait job shop problem DOI
Guanlong Deng, Ming Wei, Shuning Zhang

и другие.

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122112 - 122112

Опубликована: Окт. 17, 2023

Язык: Английский

Процитировано

5

A Self-adaptive two stage iterative greedy algorithm based job scales for energy-efficient distributed permutation flowshop scheduling problem DOI
Yang Yu, Quan Zhong, Liangliang Sun

и другие.

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 92, С. 101777 - 101777

Опубликована: Ноя. 30, 2024

Язык: Английский

Процитировано

1

Multi-Strategy Discrete Teaching–Learning-Based Optimization Algorithm to Solve No-Wait Flow-Shop-Scheduling Problem DOI Open Access
Jun Li, Xinxin Guo, Qiwen Zhang

и другие.

Symmetry, Год журнала: 2023, Номер 15(7), С. 1430 - 1430

Опубликована: Июль 17, 2023

To address the problems of single evolutionary approach, decreasing diversity, inhomogeneity, and meaningfulness in destruction process when teaching–learning-based optimization (TLBO) algorithm solves no-wait flow-shop-scheduling problem, multi-strategy discrete (MSDTLBO) is introduced. Considering differences between individuals, redefined from student’s point view, giving basic integer sequence encoding. fact that prone to falling into local optimum leading a reduction search accuracy, population was divided three groups according learning ability different teaching strategies were adopted achieve effect their needs. improve destruction-and-reconstruction with symmetry, an iterative greedy destruction–reconstruction used as main body, knowledge base control number meaningless artifacts be destroyed dynamically change artifact-selection method process. Finally, applied problem (NWFSP) test its practical application value. After comparing twenty-one benchmark functions six algorithms, experimental results showed has certain effectiveness solving NWFSP.

Язык: Английский

Процитировано

3

A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem DOI
Rapeepan Pitakaso, Kanchana Sethanan, Kim Hua Tan

и другие.

Annals of Operations Research, Год журнала: 2023, Номер 342(1), С. 621 - 656

Опубликована: Июнь 5, 2023

Язык: Английский

Процитировано

2

An Improved Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem DOI Creative Commons
Alfonsas Misevičius,

Aleksandras Andrejevas,

Armantas Ostreika

и другие.

Mathematics, Год журнала: 2024, Номер 12(23), С. 3726 - 3726

Опубликована: Ноя. 27, 2024

In this paper, an improved hybrid genetic-hierarchical algorithm for the solution of quadratic assignment problem (QAP) is presented. The based on genetic search combined with hierarchical (hierarchicity-based multi-level) iterated tabu procedure. following are two main scientific contributions paper: (i) enhanced two-level primary (master)-secondary (slave) proposed; (ii) augmented universalized multi-strategy perturbation (mutation process)—which integrated within a multi-level algorithm—is implemented. proposed scheme enables efficient balance between intensification and diversification in process. computational experiments have been conducted using QAP instances sizes up to 729. results from demonstrate outstanding performance new approach. This especially obvious small- medium-sized instances. Nearly 90% runs resulted (pseudo-)optimal solutions. Three best-known solutions achieved very hard, challenging

Язык: Английский

Процитировано

0