Self-organized Parallel Cooperation for Solving Optimization Problems

2009 ◽  
pp. 135-145
Author(s):  
Sanaz Mostaghim ◽  
Hartmut Schmeck
Keyword(s):  
Optimization Problems ◽  
Self Organized

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 review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

Generalized External Optimization

2005 ◽  
pp. 41-60 ◽  
Author(s):  
Fabiano Luis de Sousa ◽  
Fernando Manuel Ramos ◽  
Roberto Luiz Galski ◽  
Issamu Muraoka
Keyword(s):  
Optimization Problems ◽  
Evolutionary Model ◽  
Design Tool ◽  
Thermal Design ◽  
Extremal Optimization ◽  
Integer Variables ◽  
Constrained Problems ◽  
Self Organized ◽  
Complex Design ◽  
Self Organized Criticality

In this chapter a recently proposed meta-heuristic devised to be used in complex optimization problems is presented. Called Generalized Extremal Optimization (GEO), it was inspired by a simple co-evolutionary model, developed to show the emergence of self-organized criticality in ecosystems. The algorithm is of easy implementation, does not make use of derivatives and can be applied to unconstrained or constrained problems, non-convex or even disjoint design spaces, with any combination of continuous, discrete or integer variables. It is a global search meta-heuristic, like the Genetic Algorithm (GA) and the Simulated Annealing (SA), but with the advantage of having only one free parameter to adjust. The GEO has been shown to be competitive to the GA and the SA in tackling complex design spaces and a useful tool in real design problems. Here the algorithm is described, including a step-by-step implementation to a simple numerical example, its main characteristics highlighted, and its efficacy as a design tool illustrated with an application to satellite thermal design.

scholarly journals Parallelized Swarm Intelligence Approach for Solving TSP and JSSP Problems

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 142
Author(s):  
Piotr Jedrzejowicz ◽  
Izabela Wierzbowska
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Special Class ◽  
Computational Experiment ◽  
Optimization Problems ◽  
Population Based ◽  
Apache Spark ◽  
Self Organized ◽  
Intelligence Approach

One of the possible approaches to solving difficult optimization problems is applying population-based metaheuristics. Among such metaheuristics, there is a special class where searching for the best solution is based on the collective behavior of decentralized, self-organized agents. This study proposes an approach in which a swarm of agents tries to improve solutions from the population of solutions. The process is carried out in parallel threads. The proposed algorithm—based on the mushroom-picking metaphor—was implemented using Scala in an Apache Spark environment. An extended computational experiment shows how introducing a combination of simple optimization agents and increasing the number of threads may improve the results obtained by the model in the case of TSP and JSSP problems.

An Efficient Topology Optimization Method for Structures with Uniform Stress

2018 ◽  
Vol 15 (08) ◽  
pp. 1850073 ◽  
Author(s):  
Sheng Chu ◽  
Liang Gao ◽  
Mi Xiao
Keyword(s):  
Topology Optimization ◽  
Decision Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Bisection Method ◽  
Uniform Stress ◽  
Topology Optimization Problem ◽  
Self Organized ◽  
Volume Minimization ◽  
Single Objective

This paper focuses on two kinds of bi-objective topology optimization problems with uniform-stress constraints: compliance-volume minimization and local frequency response–volume minimization problems. An adaptive volume constraint (AVC) algorithm based on an improved bisection method is proposed. Using this algorithm, the bi-objective uniform-stress-constrained topology optimization problem is transformed into a single-objective topology optimization problem and a volume-decision problem. The parametric level set method based on the compactly supported radial basis functions is employed to solve the single-objective problem, in which a self-organized acceleration scheme based on shape derivative and topological sensitivity is proposed to adaptively adjust the derivative of the objective function and the step length during the optimization. To solve the volume-decision problem, an improved bisection method is proposed. Numerical examples are tested to illustrate the feasibility and effectiveness of the self-organized acceleration scheme and the AVC algorithm based on the improved bisection method. An extended application to the bi-objective stress-constrained topology optimization of a structure with stress concentration is also presented.

scholarly journals Joint Optimization Framework For Maximization of Instantaneous Transmission Rate In Signal To Interference Noise Ratio Constrained UAVs-Supported Self-Organized Device-To-Device Network

2021 ◽  
Author(s):  
Abhishek Mondal ◽  
Ashraf Hossain
Keyword(s):  
Optimization Problems ◽  
Transmission Rate ◽  
Signal Propagation ◽  
Joint Optimization ◽  
Current Policy ◽  
Optimization Framework ◽  
Self Organized ◽  
Interference Noise ◽  
Device To Device ◽  
Noise Ratio

Abstract Due to their high maneuverability, flexible deployment, and line of sight (LoS) transmission, unmanned aerial vehicles (UAVs) could be an alternative option for reliable device-to-device (D2D) communication when a direct link is not available between source and destination devices due to obstacles in the signal propagation path. Therefore, in this paper, we have proposed a UAVs-supported self-organized device-to-device (USSD2D) network where multiple UAVs are employed as aerial relays. We have developed a novel optimization framework that maximizes the total instantaneous transmission rate of the network by jointly optimizing the deployed location of UAVs, device association, and UAVs’ channel selection while ensuring that every device should achieve a given signal to interference noise ratio (SINR) constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed into a Markov decision process (MDP) where UAVs learn the system parameters according to the current state and corresponding action aiming to maximize the generated reward under the current policy. Finally, we conceive SARSA, a low complexity iterative algorithm for updating the current policy in the case of randomly deployed device pairs which achieves a good computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on the optimal deployment of UAVs. The proposed methodology improves the total instantaneous transmission rate of the network by 75.37%, 52.08%, and 14.77% respectively as compared with RS-FORD, ES-FIRD, and AOIV schemes.

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

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