scholarly journals Social Behaviour Inspired Optimization Algorithm: An Approach for Solving Complex Optimization Problems

Helix ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 3985-3988
Author(s):  
Priya Chandel
Keyword(s):  
Optimization Algorithm ◽  
Social Behaviour ◽  
Optimization Problems ◽  
Complex Optimization

A novel collaborative optimization algorithm in solving complex optimization problems

2016 ◽  
Vol 21 (15) ◽  
pp. 4387-4398 ◽  
Author(s):  
Wu Deng ◽  
Huimin Zhao ◽  
Li Zou ◽  
Guangyu Li ◽  
Xinhua Yang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Collaborative Optimization ◽  
Complex Optimization

scholarly journals Penyelesaian Container Stowage Problem untuk Kontainer Ukuran 20 Feet menggunakan Whale Optimization Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 100
Author(s):  
Quinn Nathania PJY ◽  
Asri Bekti Pratiwi ◽  
Herry Suprajitno
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Small Data ◽  
C Programming Language ◽  
Whale Optimization ◽  
C Programming ◽  
Complex Optimization ◽  
Unloading Time

This paper has purpose to solve Container Stowage Problem (CSP) for 20 feet container using Whale Optimization Algorithm (WOA). CSP is a problem discussing about how to stowage a container on the ship where the purpose to minimize the unloading time. Moreover, 20 feet container is one of container types. WOA is a recently developed swarm-based metaheuristic algorithm that is based on the bubble net hunting maneuver technique of humpback whales for solving complex optimization problems. WOA had three procedures, first encircling prey, second bubble-net attacking method or exploitation phase, and third search for prey or exploration phase. WOA application program or resolving solve CSP for 20 feet container was made by using Borland C++ programming language which was implemented in three cases types of CSP data, first, the small data taking about nine containers with the number of  bays, rows and tiers, respectively, are 4, 4, 4. The second and third data was medium data and big data with 62 containers and 95 containers each data, and had the number of bays, rows and tiers, respectively, are 14, 4, 5. After executing the program can be concluded the unloading time will be better if the number of whales is larger, while the number of iterations and the number of parameter control for shape of a logaritma spiral  don’t affect the solution.

scholarly journals Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm

2015 ◽  
Vol 3 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Maziar Yazdani ◽  
Fariborz Jolai
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Benchmark Problems ◽  
The Past ◽  
Complex Optimization ◽  
Lion Optimization Algorithm ◽  
Population Based Algorithm

Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

scholarly journals Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea

2020 ◽  
Vol 10 (11) ◽  
pp. 3970 ◽  
Author(s):  
Mohammad Nasir ◽  
Ali Sadollah ◽  
Jin Hee Yoon ◽  
Zong Woo Geem
Keyword(s):  
Comparative Study ◽  
Computer Science ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Fields Of Study ◽  
Complex Optimization ◽  
Near Future

Harmony Search (HS) is a music-inspired optimization algorithm for solving complex optimization problems that imitate the musical improvisational process. This paper reviews the potential of applying the HS algorithm in three countries, China, South Korea, and Japan. The applications represent several disciplines in fields of study such as computer science, mathematics, electrical/electronic, mechanical, chemical, civil, and industrial engineering. We anticipate an increasing number of HS applications from these countries in near future.

scholarly journals LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Dan Shan ◽  
GuoHua Cao ◽  
HongJiang Dong
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fruit Fly ◽  
Experimental Results ◽  
Fruit Fly Optimization Algorithm ◽  
Nonlinear Functions ◽  
Fruit Fly Optimization ◽  
Complex Optimization ◽  
Simulation Results

Recently, a new fruit fly optimization algorithm (FOA) is proposed to solve optimization problems. In this paper, we empirically study the performance of FOA. Six different nonlinear functions are selected as testing functions. The experimental results illustrate that FOA cannot solve complex optimization problems effectively. In order to enhance the performance of FOA, an improved FOA (named LGMS-FOA) is proposed. Simulation results and comparisons of LGMS-FOA with FOA and other metaheuristics show that LGMS-FOA can greatly enhance the searching efficiency and greatly improve the searching quality.

scholarly journals Hierarchical Swarm Model: A New Approach to Optimization

2010 ◽  
Vol 2010 ◽  
pp. 1-30 ◽  
Author(s):  
Hanning Chen ◽  
Yunlong Zhu ◽  
Kunyuan Hu ◽  
Xiaoxian He
Keyword(s):  
Complex System ◽  
Evolutionary Algorithms ◽  
Performance Improvement ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Swarm Optimization ◽  
New Approach ◽  
Proposed Model ◽  
Complex Optimization ◽  
Different Levels

This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named ), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

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