scholarly journals A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1903
Author(s):  
Edwards Cerqueira de Castro ◽  
Evandro Ottoni Teatini Salles ◽  
Patrick Marques Ciarelli
Keyword(s):  
Success Rate ◽  
Swarm Intelligence ◽  
Processing Time ◽  
Optimization Problems ◽  
Solution Space ◽  
Dynamic Optimization Problems ◽  
Shuffled Frog Leaping Algorithm ◽  
New Approach ◽  
Shuffled Frog Leaping

This work proposes a new approach to improve swarm intelligence algorithms for dynamic optimization problems by promoting a balance between the transfer of knowledge and the diversity of particles. The proposed method was designed to be applied to the problem of video tracking targets in environments with almost constant lighting. This approach also delimits the solution space for a more efficient search. A robust version to outliers of the double exponential smoothing (DES) model is used to predict the target position in the frame delimiting the solution space in a more promising region for target tracking. To assess the quality of the proposed approach, an appropriate tracker for a discrete solution space was implemented using the meta-heuristic Shuffled Frog Leaping Algorithm (SFLA) adapted to dynamic optimization problems, named the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). The DSFLA was compared with other classic and current trackers whose algorithms are based on swarm intelligence. The trackers were compared in terms of the average processing time per frame and the area under curve of the success rate per Pascal metric. For the experiment, we used a random sample of videos obtained from the public Hanyang visual tracker benchmark. The experimental results suggest that the DSFLA has an efficient processing time and higher quality of tracking compared with the other competing trackers analyzed in this work. The success rate of the DSFLA tracker is about 7.2 to 76.6% higher on average when comparing the success rate of its competitors. The average processing time per frame is about at least 10% faster than competing trackers, except one that was about 26% faster than the DSFLA tracker. The results also show that the predictions of the robust DES model are quite accurate.

Multiple Target Exploration Approach for Design Exploration Using a Swarm Intelligence and Clustering

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Hyeongmin Han ◽  
Sehyun Chang ◽  
Harrison Kim
Keyword(s):  
Design Problem ◽  
Optimization Problems ◽  
Complete Solution ◽  
Solution Space ◽  
Analytical Models ◽  
Target Point ◽  
Design Exploration ◽  
Exploration Process ◽  
Engineering Design Problems

In engineering design problems, performance functions evaluate the quality of designs. Among the designs, some of them are classified as good designs if responses from performance functions satisfy a target point or range. An infinite set of good designs in the design space is defined as a solution space of the design problem. In practice, since the performance functions are analytical models or black-box simulations which are computationally expensive, it is difficult to obtain a complete solution space. In this paper, a method that finds a finite set of good designs, which is included in a solution space, is proposed. The method formulates the problem as optimization problems and utilizes gray wolf optimizer (GWO) in the way of design exploration. Target points of the exploration process are defined by clustering intermediate solutions for every iteration. The method is tested with a simple two-dimensional problem and an automotive vehicle design problem to validate and check the quality of solution points.

Enhanced Shuffled Frog Leaping Algorithm with Modified Memeplexes

2020 ◽  
Vol 10 ◽  
Author(s):  
Tarun Kumar Sharma ◽  
Ajith Abraham ◽  
Jitendra Rajpurohit
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Standard Test ◽  
Shuffled Frog Leaping Algorithm ◽  
Local Optima ◽  
Acceleration Rate ◽  
New Variant ◽  
Shuffled Frog Leaping ◽  
Level Of Significance ◽  
Better Than

Aims: To design a new variant of Shuffled Frog Leaping Algorithm in which memeplexes formation is modified with new strategy. Background: Shuffled frog leaping (SFL) is a memetic meta-heuristic algorithm that inherits the features of two other algorithms. Its intensification component of search is similar to Particle Swarm Optimization while the inspiration for diversification is inherited from the global exchange of information in Shuffled Complex Evolution. Basic variant has been applied to solve many optimisation problems. SFLA suffers with slow acceleration rate. Objective: To propose a robust hybrid SFLA that accelerates convergence. Method: Two modifications are proposed in the structure of basic SFLA. Firstly, memeplexes formation is modified to handle continuous optimization problems. Secondly, in basic SFL algorithm the position of worst frog is improved by moving it towards the best frog in the respective memeplex, with the progress of execution, the difference between best and worst frog position reduces; there may be more chances to trap in local minima. With an aim to improve convergence and avoiding trapping in local optima a parent centric operator is embedded in each memeplex while performing a local search. The proposed algorithm is named as PC-SFLA (Parent Centric - Shuffled frog leaping algorithm) Result: The improved efficiency of PC-SFLA is validated on a robust and diverse set of standard test functions defined in CEC 2006 and 2010 and further its efficacy is verified to optimize the total cost of Supply chain management of a system. Non-parametric statistical result analysis demonstrates the efficiency of the proposal. Conclusion: PC-SFLA performed better than PSO, DE, PESO+, Modified DE, ABC and SFLA at 5% and 10% level of significance where as at par with Shuffled-ABC for g01-g07 functions of CEC 2006 in terms of NFE’s. Similarly, PCSFLA performed better than SaDE, SFLA, CMODE at both level of significance (5% & 10%) and at par with MPDE in terms of mean function value for 17 problems taken from CEC 2006. Further PC-SFLA is investigated on a set of 18 problems from CEC 2010 and Wilcoxon signed ranks test is performed at 5% level of significance. PC-SFLA performed better than SFLA and CHDE and at par with PESO. The computational results present the competency of the proposed method to solve quadratic, nonlinear, polynomial, linear as well as cubic functions efficiently. The simulated results shows that the proposed algorithm is capable of solving mix integer constrained continuous optimization problem efficiently.

Multi-objective parameter optimization of the HYPE model using shuffled frog-leaping algorithm

2021 ◽  
Author(s):  
Xinyu Li ◽  
Prajna Kasargodu Anebgailu ◽  
Jörg Dietrich
Keyword(s):  
Optimization Problems ◽  
Population Based ◽  
Optimization Techniques ◽  
Model Parameters ◽  
Hydrological Models ◽  
Watershed Model ◽  
Shuffled Frog Leaping Algorithm ◽  
Multi Objective ◽  
Combinatorial Optimization Problems ◽  
Shuffled Frog Leaping

<p>The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.</p><p>In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.</p>

Performance Studies on Differential Evolution Algorithm and Shuffled Frog-Leaping Algorithm for Simulated Manufacturing Problems

2016 ◽  
Vol 835 ◽  
pp. 858-863 ◽  
Author(s):  
Lakkana Ruekkasaem ◽  
Pasura Aungkulanon
Keyword(s):  
Differential Evolution ◽  
Performance Studies ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Differential Evolution Algorithm ◽  
Shuffled Frog Leaping Algorithm ◽  
Linear Optimization Problems ◽  
Shuffled Frog Leaping ◽  
Manufacturing Problems ◽  
Maximum Minimum

The real world engineering problems are complex associated with lot of factors. The objective of mathematic models in simulated manufacturing problems are to minimize cost or maximize profits while satisfying the constraints. The purpose of this article was to study two algorithms for testing their efficiency in solving non-linear optimization problems and simulated manufacturing problems. A well-known meta-heuristic approach called Differential Evolution (DE) was compared with Shuffled Frog-leaping Algorithm (SFLA) in term of mean, maximum, minimum, and standard deviation of the solution. SFLA was better than DE in terms of the performance to finding optimal solutions because of the unique process of memeplex, which can increase speed of convergence and find turning parameters.

scholarly journals A New Approach Based on K-means Clustering and Shuffled Frog Leaping Algorithm to Solve Travelling Salesman Problem

2019 ◽  
Vol 2 (3) ◽  
pp. 446-453
Author(s):  
Murat Karakoyun
Keyword(s):  
Shortest Path ◽  
Travelling Salesman Problem ◽  
Experimental Results ◽  
Hard Problem ◽  
Shuffled Frog Leaping Algorithm ◽  
Travelling Salesman ◽  
New Approach ◽  
Starting Point ◽  
Np Hard Problem ◽  
Shuffled Frog Leaping

The Travelling Salesman Problem (TSP), which is a combinatorial NP-hard problem, aims to find the shortest possible path while visiting all cities (only once) in a given list and returns to the starting point. In this paper, an approach, which is based on k-means clustering and Shuffled Frog Leaping Algorithm (SFLA), is used to solve the TSP. The proposed approach consists of three parts: separate the cities into k clusters, find the shortest path for each cluster and merge the clusters. Experimental results have shown that the algorithm get better results as the number of cluster increase for problems that have a large number of cities.

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