Parameter Selection for Swarm Intelligence Algorithms

2018 ◽  
Vol 9 (4) ◽  
pp. 1-20
Author(s):  
Breno A. M. Menezes ◽  
Fabian Wrede ◽  
Herbert Kuchen ◽  
Fernando B. Lima Neto
Keyword(s):  
Population Size ◽  
Swarm Intelligence ◽  
Execution Time ◽  
Optimization Problems ◽  
Parallel Implementation ◽  
Fish School ◽  
Size Number ◽  
Complex Optimization ◽  
Selection For ◽  
Number Of Iterations

Swarm intelligence (SI) algorithms are handy tools for solving complex optimization problems. When problems grow in size and complexity, an increase in population or number of iterations might be required in order to achieve a good solution. These adjustments also impact the execution time. This article investigates the trade-off involving population size, number of iterations and problem complexity, aiming to improve the efficiency of SI algorithms. Results based on a parallel implementation of Fish School Search show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior, i.e. increasing the population over a certain threshold only leads to slight improvements. Furthermore, the execution time was analyzed.

Multi-Objective Binary Fish School Search

2018 ◽  
pp. 53-72
Author(s):  
Mariana Gomes da Motta Macedo ◽  
Carmelo J. A. Bastos-Filho ◽  
Susana M. Vieira ◽  
João M. C. Sousa
Keyword(s):  
Feature Selection ◽  
Optimization Problems ◽  
Objective Functions ◽  
Binary Decision ◽  
Fish School ◽  
Multi Objective ◽  
Selection For ◽  
Input Variables ◽  
Threshold Technique ◽  
State Of Art

Fish school search (FSS) algorithm has inspired several adaptations for multi-objective problems or binary optimization. However, there is no particular proposition to solve both problems simultaneously. The proposed multi-objective approach binary fish school search (MOBFSS) aims to solve optimization problems with two or three conflicting objective functions with binary decision input variables. MOBFSS is based on the dominance concept used in the multi-objective fish school search (MOFSS) and the threshold technique deployed in the binary fish school search (BFSS). Additionally, the authors evaluate the proposal for feature selection for classification in well-known datasets. Moreover, the authors compare the performance of the proposal with a state-of-art algorithm called BMOPSO-CDR. MOBFSS presents better results than BMOPSO-CDR, especially for datasets with higher complexity.

Lifecycle coevolution framework for many evolutionary and swarm intelligence algorithms fusion in solving complex optimization problems

2019 ◽  
Vol 47 ◽  
pp. 3-20 ◽  
Author(s):  
Maowei He ◽  
Yabao Hu ◽  
Hanning Chen ◽  
Liling Sun ◽  
Xiaoxuan Wang ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Complex Optimization

scholarly journals A Comparative Study of Swarm Intelligence Algorithms for UCAV Path-Planning Problems

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 171
Author(s):  
Haoran Zhu ◽  
Yunhe Wang ◽  
Zhiqiang Ma ◽  
Xiangtao Li
Keyword(s):  
Path Planning ◽  
Swarm Intelligence ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Planning Problem ◽  
Complex Optimization ◽  
Battlefield Environment ◽  
Planning Problems ◽  
Path Planning Problem ◽  
Open Issues

Path-planning for uninhabited combat air vehicles (UCAV) is a typically complicated global optimization problem. It seeks a superior flight path in a complex battlefield environment, taking into various constraints. Many swarm intelligence (SI) algorithms have recently gained remarkable attention due to their capability to address complex optimization problems. However, different SI algorithms present various performances for UCAV path-planning since each algorithm has its own strengths and weaknesses. Therefore, this study provides an overview of different SI algorithms for UCAV path-planning research. In the experiment, twelve algorithms that published in major journals and conference proceedings are surveyed and then applied to UCAV path-planning. Moreover, to demonstrate the performance of different algorithms in further, we design different scales of problem cases for those comparative algorithms. The experimental results show that UCAV can find the safe path to avoid the threats efficiently based on most SI algorithms. In particular, the Spider Monkey Optimization is more effective and robust than other algorithms in handling the UCAV path-planning problem. The analysis from different perspectives contributes to highlight trends and open issues in the field of UCAVs.

scholarly journals Solving knapsack problems using a binary gaining sharing knowledge-based optimization algorithm

2021 ◽  
Author(s):  
Prachi Agrawal ◽  
Talari Ganesh ◽  
Ali Wagdy Mohamed
Keyword(s):  
Life Span ◽  
Population Size ◽  
Linear Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Knapsack Problems ◽  
Local Optima ◽  
Knowledge Based ◽  
Two Stages

AbstractThis article proposes a novel binary version of recently developed Gaining Sharing knowledge-based optimization algorithm (GSK) to solve binary optimization problems. GSK algorithm is based on the concept of how humans acquire and share knowledge during their life span. A binary version of GSK named novel binary Gaining Sharing knowledge-based optimization algorithm (NBGSK) depends on mainly two binary stages: binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable NBGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. Moreover, to enhance the performance of NBGSK and prevent the solutions from trapping into local optima, NBGSK with population size reduction (PR-NBGSK) is introduced. It decreases the population size gradually with a linear function. The proposed NBGSK and PR-NBGSK applied to set of knapsack instances with small and large dimensions, which shows that NBGSK and PR-NBGSK are more efficient and effective in terms of convergence, robustness, and accuracy.

scholarly journals Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

2021 ◽  
Vol 11 (8) ◽  
pp. 3430
Author(s):  
Erik Cuevas ◽  
Héctor Becerra ◽  
Héctor Escobar ◽  
Alberto Luque-Chang ◽  
Marco Pérez ◽  
...  
Keyword(s):  
Convergence Rates ◽  
Optimization Problems ◽  
Search Space ◽  
Second Order ◽  
Order System ◽  
Search Patterns ◽  
Multiple Behaviors ◽  
Complex Optimization ◽  
Second Order Systems ◽  
Order Algorithm

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

scholarly journals High-Level Parallel Ant Colony Optimization with Algorithmic Skeletons

2021 ◽  
Author(s):  
Breno A. de Melo Menezes ◽  
Nina Herrmann ◽  
Herbert Kuchen ◽  
Fernando Buarque de Lima Neto
Keyword(s):  
Ant Colony Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Programming Model ◽  
Parallel Implementation ◽  
Ant Colony ◽  
Algorithmic Skeletons ◽  
Low Level ◽  
Programming Patterns ◽  
High Level

AbstractParallel implementations of swarm intelligence algorithms such as the ant colony optimization (ACO) have been widely used to shorten the execution time when solving complex optimization problems. When aiming for a GPU environment, developing efficient parallel versions of such algorithms using CUDA can be a difficult and error-prone task even for experienced programmers. To overcome this issue, the parallel programming model of Algorithmic Skeletons simplifies parallel programs by abstracting from low-level features. This is realized by defining common programming patterns (e.g. map, fold and zip) that later on will be converted to efficient parallel code. In this paper, we show how algorithmic skeletons formulated in the domain specific language Musket can cope with the development of a parallel implementation of ACO and how that compares to a low-level implementation. Our experimental results show that Musket suits the development of ACO. Besides making it easier for the programmer to deal with the parallelization aspects, Musket generates high performance code with similar execution times when compared to low-level implementations.

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