Efficient Parameter Optimization Based on Combination of Direct Global and Local Search Methods

1999 ◽  
pp. 227-249 ◽  
Author(s):  
Michael Syrjakow ◽  
Helena Szczerbicka
Keyword(s):  
Local Search ◽  
Parameter Optimization ◽  
Search Methods ◽  
Local Search Methods ◽  
Global And Local

On the hybridization of global and local search methods

2018 ◽  
Vol 35 (3) ◽  
pp. 3451-3464 ◽  
Author(s):  
Rashida Adeeb Khanum ◽  
Muhammad Asif Jan ◽  
Wali Khan Mashwani ◽  
Nasser Mansoor Tairan ◽  
Hidayat Ullah Khan ◽  
...  
Keyword(s):  
Local Search ◽  
Search Methods ◽  
Local Search Methods ◽  
Global And Local

scholarly journals Planning capacity for 5G and beyond wireless networks by discrete fireworks algorithm with ensemble of local search methods

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Hafiz Munsub Ali ◽  
Jiangchuan Liu ◽  
Waleed Ejaz
Keyword(s):  
Wireless Networks ◽  
Local Search ◽  
Wireless Network ◽  
Statistical Tests ◽  
Mathematical Framework ◽  
Integer Programming Problem ◽  
Search Methods ◽  
Fireworks Algorithm ◽  
Local Search Methods ◽  
Planning Capacity

Abstract In densely populated urban centers, planning optimized capacity for the fifth-generation (5G) and beyond wireless networks is a challenging task. In this paper, we propose a mathematical framework for the planning capacity of a 5G and beyond wireless networks. We considered a single-hop wireless network consists of base stations (BSs), relay stations (RSs), and user equipment (UEs). Wireless network planning (WNP) should decide the placement of BSs and RSs to the candidate sites and decide the possible connections among them and their further connections to UEs. The objective of the planning is to minimize the hardware and operational cost while planning capacity of a 5G and beyond wireless networks. The formulated WNP is an integer programming problem. Finding an optimal solution by using exhaustive search is not practical due to the demand for high computing resources. As a practical approach, a new population-based meta-heuristic algorithm is proposed to find a high-quality solution. The proposed discrete fireworks algorithm (DFWA) uses an ensemble of local search methods: insert, swap, and interchange. The performance of the proposed DFWA is compared against the low-complexity biogeography-based optimization (LC-BBO), the discrete artificial bee colony (DABC), and the genetic algorithm (GA). Simulation results and statistical tests demonstrate that the proposed algorithm can comparatively find good-quality solutions with moderate computing resources.

scholarly journals Solving Scheduling Problem in a Distributed Manufacturing System Using a Discrete Fruit Fly Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3260 ◽  
Author(s):  
Zhang ◽  
Liu ◽  
Tang ◽  
Królczyk ◽  
Li
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Fruit Fly ◽  
Small Scale ◽  
Fruit Fly Optimization Algorithm ◽  
Distributed Manufacturing ◽  
Scheduling Optimization ◽  
Search Methods ◽  
Fruit Fly Optimization ◽  
Local Search Methods

This study attempts to optimize the scheduling decision to save production cost (e.g., energy consumption) in a distributed manufacturing environment that comprises multiple distributed factories and where each factory has one flow shop with blocking constraints. A new scheduling optimization model is developed based on a discrete fruit fly optimization algorithm (DFOA). In this new evolutionary optimization method, three heuristic methods were proposed to initialize the DFOA model with good quality and diversity. In the smell-based search phase of DFOA, four neighborhood structures according to factory reassignment and job sequencing adjustment were designed to help explore a larger solution space. Furthermore, two local search methods were incorporated into the framework of variable neighborhood descent (VND) to enhance exploitation. In the vision-based search phase, an effective update criterion was developed. Hence, the proposed DFOA has a large probability to find an optimal solution to the scheduling optimization problem. Experimental validation was performed to evaluate the effectiveness of the proposed initialization schemes, neighborhood strategy, and local search methods. Additionally, the proposed DFOA was compared with well-known heuristics and metaheuristics on small-scale and large-scale test instances. The analysis results demonstrate that the search and optimization ability of the proposed DFOA is superior to well-known algorithms on precision and convergence.

scholarly journals New local search methods for partial MaxSAT

2016 ◽  
Vol 240 ◽  
pp. 1-18 ◽  
Author(s):  
Shaowei Cai ◽  
Chuan Luo ◽  
Jinkun Lin ◽  
Kaile Su
Keyword(s):  
Local Search ◽  
Search Methods ◽  
Local Search Methods
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