scholarly journals Solving channel assignment problems using local search methods and simulated annealing

2011 ◽  
Author(s):  
Lipo Wang ◽  
Sally N. S. Lee ◽  
Wong Y. Hing
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Channel Assignment ◽  
Assignment Problems ◽  
Search Methods ◽  
Local Search Methods

scholarly journals BEO: Binary Equilibrium Optimizer Combined with Simulated Annealing for Feature Selection

2020 ◽  
Author(s):  
Kushal Kanti Ghosh ◽  
Ritam Guha ◽  
Suman Kumar Bera ◽  
Ram Sarkar ◽  
Seyedali Mirjalili
Keyword(s):  
Feature Selection ◽  
Simulated Annealing ◽  
Transfer Function ◽  
Local Search ◽  
Search Methods ◽  
Selection Problems ◽  
Local Search Methods ◽  
Wide Range ◽  
Binary Problems

Abstract This work proposed a binary variant of the recently-proposed Equilibrium Optimizer (EO) to solve binary problems. A v-shaped transfer function is used to map continuous values created in EO to binary. To improve the exploitation of the Binary Equilibrium Optimizer (BEO), the Simulated Annealing is used as one of the most popular local search methods. The proposed BEO algorithm is applied to 18 UCI datasets and compared to a wide range of algorithms. The results demonstrate the superiority and merits of EO when solving feature selection problems.

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.

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