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 feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time DOI
Zhongshi Shao, Weishi Shao,

Jianrui Chen

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 131, С. 107818 - 107818

Опубликована: Янв. 9, 2024

Язык: Английский

Процитировано

11

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems DOI Creative Commons
Bilal Khurshid, Shahid Maqsood,

Yahya Khurshid

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Янв. 29, 2024

Abstract This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as objective function. To address complexity of this NP-hard problem, HES-IG combines evolution strategies (ES) iterated greedy (IG) algorithm, hybridizing algorithms helps different mitigate their weaknesses leverage respective strengths. The ES begins with random initial solution uses an insertion mutation to optimize solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. selection process employs (µ λ)-ES, allowing excellent solutions survive multiple generations until better surpasses them. IG algorithm’s straightforward search mechanism aids in further improving avoiding local minima. destruction operator randomly removes d-jobs, which are then inserted by construction operator. single approach, while acceptance–rejection criteria based on constant temperature. Parameters both calibrated Multifactor analysis variance technique. performance other Wilcoxon signed test. tested 21 Nos. Reeves 30 Taillard benchmark problems. has found 15 lower bound values for Similarly, Computational results indicate outperforms available techniques literature all sizes.

Язык: Английский

Процитировано

5

A cooperative grey wolf optimizer for the joint flowshop scheduling problem with sequence-dependent set-up time DOI
Shuilin Chen, Jianguo Zheng, Wenqiu Zhang

и другие.

Engineering Optimization, Год журнала: 2024, Номер unknown, С. 1 - 23

Опубликована: Апрель 12, 2024

With the complexity involved in manufacturing products, many companies use multiple processes to complete product processing. Most studies have been concerned with single production but neglected widespread joint flowshop scheduling problem (JFSP). In this article, a cooperative grey wolf optimizer (CGWO) is developed solve JFSP. First, according features of JFSP, corresponding mathematical model constructed, and three collaborative strategies random generation are proposed initialize population. process searching for prey, discretized search prey update mechanism proposed, which conducive balancing exploration exploitation. An energy-saving strategy decrease energy consumption. Moreover, four local mechanisms different optimization objectives enhance performance method attacking prey. The results show that CGWO effective solving

Язык: Английский

Процитировано

5

Adaptive variable neighborhood search algorithm with Metropolis rule and tabu list for satellite range scheduling problem DOI
Tianyu Wang, Yi Gu,

Huilin Wang

и другие.

Computers & Operations Research, Год журнала: 2024, Номер 170, С. 106757 - 106757

Опубликована: Июнь 29, 2024

Язык: Английский

Процитировано

5

Guided Genetic Algorithm for Solving Capacitated Vehicle Routing Problem With Unmanned-Aerial-Vehicles DOI Creative Commons
Ali Najm Jasim, Lamia Chaari Fourati

IEEE Access, Год журнала: 2024, Номер 12, С. 106333 - 106358

Опубликована: Янв. 1, 2024

This study proposes a capacitated vehicle routing problem (CVRP) approach to optimise Vehicle Routing Problem (VRP) and pesticides spraying. The VRP consists of finding the route which covers every point certain area interest. paper considers search spraying mission, using group Unmanned Aerial Vehicles (UAVs). In this scenario, objective is minimise total battery consumption level tank not exceed their maximum capacities. A hybrid metaheuristic optimisation algorithm formulated by integrating Genetic Algorithm (GA) with guided local called genetic (GGA). performance proposed GGA compared four single-solution based algorithms (Guided Local Search [GLS], Tabu [TS], Simulated Annealing [SA], Iterated [ILS]) two population-based metaheuristics (GA Particle Swarm Optimisation [PSO] algorithm). results revealed that outperformed other in most instances. showed competitive results, closely following TS's across different scenarios. evaluation conducted analysing its mean, standard deviation, best solution, worst solution ten iterations. addition, Wilcoxon signed-rank test 36 discussion provide confirmation method beat algorithms.

Язык: Английский

Процитировано

5

A problem-specific knowledge based artificial bee colony algorithm for scheduling distributed permutation flowshop problems with peak power consumption DOI
Yuanzhen Li, Kaizhou Gao, Leilei Meng

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 126, С. 107011 - 107011

Опубликована: Авг. 30, 2023

Язык: Английский

Процитировано

10

An improved discrete Harris Hawks optimization algorithm for the no-wait job shop problem to minimize total weighted tardiness DOI

Jie Yin,

Shuning Zhang, Li Liu

и другие.

The Journal of Supercomputing, Год журнала: 2025, Номер 81(6)

Опубликована: Апрель 17, 2025

Язык: Английский

Процитировано

0

Multi-objective scheduling for surface mount technology workshop: automatic design of two-layer decomposition-based approach DOI
Biao Zhang, Zhixuan Wang, Leilei Meng

и другие.

International Journal of Production Research, Год журнала: 2025, Номер unknown, С. 1 - 21

Опубликована: Май 9, 2025

Язык: Английский

Процитировано

0

A novel MIP model and a hybrid genetic algorithm for operation outsourcing in production scheduling with carbon tax policy DOI
Melis Alpaslan Takan

Expert Systems with Applications, Год журнала: 2024, Номер 251, С. 123983 - 123983

Опубликована: Апрель 16, 2024

Язык: Английский

Процитировано

2

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