scholarly journals Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4834
Author(s):  
Rubén Crespo-Cano ◽  
Sergio Cuenca-Asensi ◽  
Eduardo Fernández ◽  
Martínez-Álvarez
Keyword(s):  
Visual Information ◽  
Statistical Tests ◽  
Pso Algorithm ◽  
Population Based ◽  
Fine Tuning ◽  
Nsga Ii ◽  
Multi Objective ◽  
Search Heuristics ◽  
Population Based Search ◽  
Nonparametric Statistical

A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics—genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)—are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.

The Relationship between Metaheuristics Stopping Criteria and Performances

2014 ◽  
Vol 5 (3) ◽  
pp. 44-70 ◽  
Author(s):  
Mohamed-Mahmoud Ould Sidi ◽  
Bénédicte Quilot-Turion ◽  
Abdeslam Kadrani ◽  
Michel Génard ◽  
Françoise Lescourret
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Statistical Tests ◽  
Particle Swarm ◽  
Computational Time ◽  
Plant Management ◽  
Stopping Criterion ◽  
Nsga Ii ◽  
Multi Objective ◽  
Crowding Distance

A major difficulty in the use of metaheuristics (i.e. evolutionary and particle swarm algorithms) to deal with multi-objective optimization problems is the choice of a convenient point at which to stop computation. Indeed, it is difficult to find the best compromise between the stopping criterion and the algorithm performance. This paper addresses this issue using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-Objective Particle Swarm Optimization with Crowding Distance (MOPSO-CD) for the model-based design of sustainable peach fruits. The optimization problem of interest contains three objectives: maximize fruit fresh mass, maximize fruit sugar content, and minimize the crack density on the fruit skin. This last objective targets a reduction in the use of fungicides and can thus enhance preservation of the environment and human health. Two versions of the NSGA-II and two of the MOPSO-CD were applied to tackle this difficult optimization problem: the original versions with a maximum number of generations used as stopping criterion and modified versions using the stopping criterion proposed by the authors of (Roudenko & Schoenauer, 2004). This second stopping criterion is based on the stabilization of the maximal crowding distance and aims to stop computation when many generations are performed without further improvement in the quality of the solutions. A benchmark consisting of four plant management scenarios has been used to compare the performances of the original versions (OV) and the modified versions (MV) of the NSGA-II and the MOPSO-CD. Twelve independent simulations were performed for each version and for each scenario, and a high number of generations were generated for the OV (e.g., 1500 for the NSGA-II and 200 for the MOPSO-CD). This paper compares the performances of the two versions of both algorithms using four standard metrics and statistical tests. For both algorithms, the MV obtained solutions similar in quality to those of the OV but after significantly fewer generations. The resulting reduction in computational time for the optimization step will provide opportunities for further studies on the sustainability of peach ideotypes.

Evolutionary Algorithm Based Multi-Objective Aerodynamics Optimization Method for Low Reynolds Number Airfoil

2014 ◽  
Vol 660 ◽  
pp. 487-491 ◽  
Author(s):  
Lavi R. Zuhal ◽  
Yohanes Bimo Dwianto ◽  
Pramudita Satria Palar
Keyword(s):  
Reynolds Number ◽  
Aerodynamic Characteristic ◽  
Low Reynolds Number ◽  
Optimization Method ◽  
Aerodynamic Characteristics ◽  
Population Based ◽  
Nsga Ii ◽  
Multi Objective ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

This paper presents the development of multi-objective population-based optimization method, called Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize the aerodynamic characteristic of a low Reynolds number airfoil. The optimization is performed by changing the shape of the airfoil to obtain geometry with the best aerodynamic characteristics. The results of the study show that the developed optimization tool, coupled with modified PARSEC parameterization, has yielded optimum airfoils with better aerodynamic characteristics compared to original airfoil. Additionally, it is found that the developed method has better performance compared to similar methods found in literature.

scholarly journals Automatic Tuning of a Retina Model for a Cortical Visual Neuroprosthesis Using a Multi-Objective Optimization Genetic Algorithm

2016 ◽  
Vol 26 (07) ◽  
pp. 1650021 ◽  
Author(s):  
Antonio Martínez-Álvarez ◽  
Rubén Crespo-Cano ◽  
Ariadna Díaz-Tahoces ◽  
Sergio Cuenca-Asensi ◽  
José Manuel Ferrández Vicente ◽  
...  
Keyword(s):  
Computational Neuroscience ◽  
Visual Information ◽  
Automatic Tuning ◽  
Neural Structure ◽  
Nsga Ii ◽  
Multi Objective ◽  
Trade Offs ◽  
Electrophysiological Recordings ◽  
Different Types ◽  
High Flexibility

The retina is a very complex neural structure, which contains many different types of neurons interconnected with great precision, enabling sophisticated conditioning and coding of the visual information before it is passed via the optic nerve to higher visual centers. The encoding of visual information is one of the basic questions in visual and computational neuroscience and is also of seminal importance in the field of visual prostheses. In this framework, it is essential to have artificial retina systems to be able to function in a way as similar as possible to the biological retinas. This paper proposes an automatic evolutionary multi-objective strategy based on the NSGA-II algorithm for tuning retina models. Four metrics were adopted for guiding the algorithm in the search of those parameters that best approximate a synthetic retinal model output with real electrophysiological recordings. Results show that this procedure exhibits a high flexibility when different trade-offs has to be considered during the design of customized neuro prostheses.

Manufacturer Order Fulfillment Based on Multi-Objective Optimization NSGA II Model

2013 ◽  
Vol 340 ◽  
pp. 136-140
Author(s):  
Liang You Shu ◽  
Ling Xiao Yang
Keyword(s):  
Optimization Model ◽  
Optimization Problems ◽  
Population Based ◽  
Pareto Optimal ◽  
Order Fulfillment ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Local Search Procedure

The aim of this paper is to study the production and delivery decision problem in the Manufacturer Order Fulfillment. Owing to the order fulfillment optimization condition of the manufacturer, the multi-objective optimization model of manufacturers' production and delivery has been founded. The solution of the multi-objective optimization model is also very difficult. Fast and Elitist Non-dominated Sorting Genetic Algorithm (NSGA II) have been applied successfully to various test and real-world optimization problems. These population based the algorithm provide a diverse set of non-dominated solutions. The obtained non-dominated set is close to the true Pareto-optimal front. But its convergence to the true Pareto-optimal front is not guaranteed. Hence SBX is used as a local search procedure. The proposed procedure is successfully applied to a special case. The results validate that the algorithm is effective to the multi-objective optimization model.

A Novel Multi-Objective Competitive Swarm Optimization Algorithm

2020 ◽  
Vol 11 (4) ◽  
pp. 114-129
Author(s):  
Prabhujit Mohapatra ◽  
Kedar Nath Das ◽  
Santanu Roy ◽  
Ram Kumar ◽  
Nilanjan Dey
Keyword(s):  
Optimization Algorithm ◽  
State Of The Art ◽  
Statistical Tests ◽  
The Other ◽  
Benchmark Problems ◽  
Dominance Relationship ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Multi Objective ◽  
External Archive

In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.

scholarly journals Self-Regulated Particle Swarm Multi-Task Optimization

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7499
Author(s):  
Xiaolong Zheng ◽  
Deyun Zhou ◽  
Na Li ◽  
Tao Wu ◽  
Yu Lei ◽  
...  
Keyword(s):  
Knowledge Transfer ◽  
Large Scale ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Pso Algorithm ◽  
Population Based ◽  
Transfer Strategies ◽  
Aerial Vehicle ◽  
Scale Optimization ◽  
Population Based Search

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

Towards a More Efficient Multi-Objective Particle Swarm Optimizer

2011 ◽  
pp. 76-105 ◽  
Author(s):  
Luis V. Santana-Quintero ◽  
Noel Ramírez-Santiago ◽  
Carlos A. Coello Coello
Keyword(s):  
Selection Pressure ◽  
Fitness Function ◽  
Scatter Search ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Problems ◽  
Particle Swarm Optimizer ◽  
Nsga Ii ◽  
Multi Objective

This chapter presents a hybrid between a particle swarm optimization (PSO) approach and scatter search. The main motivation for developing this approach is to combine the high convergence rate of the PSO algorithm with a local search approach based on scatter search, in order to have the main advantages of these two types of techniques. We propose a new leader selection scheme for PSO, which aims to accelerate convergence by increasing the selection pressure. However, this higher selection pressure reduces diversity. To alleviate that, scatter search is adopted after applying PSO, in order to spread the solutions previously obtained, so that a better distribution along the Pareto front is achieved. The proposed approach can produce reasonably good approximations of multi-objective problems of high dimensionality, performing only 4,000 fitness function evaluations. Test problems taken from the specialized literature are adopted to validate the proposed hybrid approach. Results are compared with respect to the NSGA-II, which is an approach representative of the state-of-the-art in the area.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

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