Solving continuous optimization problems using the tree seed algorithm developed with the roulette wheel strategy

2021 ◽  
pp. 114579
Author(s):  
Mehmet Beşkirli
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Roulette Wheel ◽  
Continuous Optimization Problems ◽  
Tree Seed

scholarly journals Tree-Seed Algorithm for Large-Scale Binary Optimization

2018 ◽  
Vol 3 (1) ◽  
pp. 48 ◽  
Author(s):  
Ahmet Cevahir Cinar ◽  
Hazim Iscan ◽  
Mustafa Servet Kiran
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Logic Gate ◽  
Continuous Optimization ◽  
Population Based ◽  
Benchmark Problems ◽  
Binary Optimization ◽  
Continuous Optimization Problems ◽  
Computation Algorithms ◽  
Tree Seed

Population-based swarm or evolutionary computation algorithms in optimization are attracted the interest of the researchers due their simple structure, optimization performance, easy-adaptation. Binary optimization problems can be also solved by using these algorithms. This paper focuses on solving large scale binary optimization problems by using Tree-Seed Algorithm (TSA) proposed for solving continuous optimization problems by imitating relationship between the trees and their seeds in nature. The basic TSA is modified by using xor logic gate for solving binary optimization problems in this study. In order to investigate the performance of the proposed algorithm, the numeric benchmark problems with the different dimensions are considered and obtained results show that the proposed algorithm produces effective and comparable solutions in terms of solution quality.Keywords: binary optimization, tree-seed algorithm, xor-gate, large-scale optimization

TSASC: tree–seed algorithm with sine–cosine enhancement for continuous optimization problems

2020 ◽  
Vol 24 (24) ◽  
pp. 18627-18646
Author(s):  
Jianhua Jiang ◽  
Rui Han ◽  
Xianqiu Meng ◽  
Keqin Li
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Optimization Problems ◽  
Tree Seed

A chaotic and hybrid gray wolf-whale algorithm for solving continuous optimization problems

2021 ◽  
Author(s):  
Kayvan Asghari ◽  
Mohammad Masdari ◽  
Farhad Soleimanian Gharehchopogh ◽  
Rahim Saneifard
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Gray Wolf ◽  
Continuous Optimization Problems

A Study of the Continuous Optimization Problem Using a Wood Robot Controlled by a Biologically Motivated System

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Jong-Chen Chen
Keyword(s):  
Physical Environment ◽  
Intelligent System ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Continuous Optimization ◽  
Physical Structure ◽  
Experimental Results ◽  
Self Organized ◽  
Selection Operators ◽  
Continuous Optimization Problems

Continuous optimization plays an increasingly significant role in everyday decision-making situations. Our group had previously developed a multilevel system called the artificial neuromolecular system (ANM) that possessed structure richness allowing variation and/or selection operators to act on it in order to generate a broad range of dynamic behaviors. In this paper, we used the ANM system to control the motions of a wooden walking robot named Miky. The robot was used to investigate the ANM system's capability to deal with continuous optimization problems through self-organized learning. Evolutionary learning algorithm was used to train the system and generate appropriate control. The experimental results showed that Miky was capable of learning in a continued manner in a physical environment. A further experiment was conducted by making some changes to Miky's physical structure in order to observe the system's capability to deal with the change. Detailed analysis of the experimental results showed that Miky responded to the change by appropriately adjusting its leg movements in space and time. The results showed that the ANM system possessed continuous optimization capability in coping with the change. Our findings from the empirical experiments might provide us another dimension of information of how to design an intelligent system comparatively friendlier than the traditional systems in assisting humans to walk.

scholarly journals A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

2020 ◽  
Vol 34 (05) ◽  
pp. 7111-7118
Author(s):  
Moumita Choudhury ◽  
Saaduddin Mahmud ◽  
Md. Mosaddek Khan
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Continuous Variables ◽  
Constraint Optimization ◽  
Solution Quality ◽  
Distributed Constraint Optimization ◽  
Continuous Optimization Problems ◽  
Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of several distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that can explicitly model a problem containing continuous variables. Nevertheless, state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.

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