scholarly journals An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Deyu Tang ◽  
Jie Zhao ◽  
Jin Yang ◽  
Zhen Liu ◽  
Yongming Cai
Keyword(s):  
Local Search ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Evolutionary Strategy ◽  
Quantum Evolution ◽  
Potential Wells ◽  
Shuffled Frog Leaping ◽  
Evolutionary Operator ◽  
Better Than

Shuffled frog leaping algorithm, a novel heuristic method, is inspired by the foraging behavior of the frog population, which has been designed by the shuffled process and the PSO framework. To increase the convergence speed and effectiveness, the currently improved versions are focused on the local search ability in PSO framework, which limited the development of SFLA. Therefore, we first propose a new scheme based on evolutionary strategy, which is accomplished by quantum evolution and eigenvector evolution. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search. To test the performance of the proposed approach, the basic benchmark suites, CEC2013 and CEC2014, and a parameter optimization problem of SVM are used to compare 15 well-known algorithms. Experimental results demonstrate that the performance of the proposed algorithm is better than that of the other heuristic algorithms.

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.

A Tunneling Based Method for Mixed-Discrete Constrained Nonlinear Optimization

1998 ◽  
Author(s):  
Gregory J. Kott ◽  
Gary A. Gabriele
Keyword(s):  
Optimization Problems ◽  
Heuristic Method ◽  
New Method ◽  
Second Phase ◽  
Two Phase ◽  
Design Problems ◽  
Classical Design ◽  
Continuous Problems ◽  
Discrete Optimization Problems ◽  
Better Than

Abstract This paper describes the development of a new method to solve mixed-discrete optimization problems. The method is a two phase approach similar to Tunnel Methods developed for global optimization of continuous problems. It uses a SQP optimization solver in the first phase and an efficient rounding procedure to find discrete solutions in the second phase. All components utilized in this heuristic method are implemented with an emphasis on efficiency. The method was implemented in MATLAB and the solutions of three classical design problems are given. The results show the new method is very robust in finding high quality solutions which are consistently as good or better than past published results.

scholarly journals A bi-stage surrogate-assisted hybrid algorithm for expensive optimization problems

2021 ◽  
Author(s):  
Zhihai Ren ◽  
Chaoli Sun ◽  
Ying Tan ◽  
Guochen Zhang ◽  
Shufen Qin
Keyword(s):  
Local Search ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Second Stage ◽  
Expensive Problems ◽  
Expensive Optimization Problems ◽  
Expensive Optimization

AbstractSurrogate-assisted meta-heuristic algorithms have shown good performance to solve the computationally expensive problems within a limited computational resource. Compared to the method that only one surrogate model is utilized, the surrogate ensembles have shown more efficiency to get a good optimal solution. In this paper, we propose a bi-stage surrogate-assisted hybrid algorithm to solve the expensive optimization problems. The framework of the proposed method is composed of two stages. In the first stage, a number of global searches will be conducted in sequence to explore different sub-spaces of the decision space, and the solution with the maximum uncertainty in the final generation of each global search will be evaluated using the exact expensive problems to improve the accuracy of the approximation on corresponding sub-space. In the second stage, the local search is added to exploit the sub-space, where the best position found so far locates, to find a better solution for real expensive evaluation. Furthermore, the local and global searches in the second stage take turns to be conducted to balance the trade-off of the exploration and exploitation. Two different meta-heuristic algorithms are, respectively, utilized for the global and local search. To evaluate the performance of our proposed method, we conduct the experiments on seven benchmark problems, the Lennard–Jones potential problem and a constrained test problem, respectively, and compare with five state-of-the-art methods proposed for solving expensive problems. The experimental results show that our proposed method can obtain better results, especially on high-dimensional problems.

Inspirations from Nature for Meta-heuristic Algorithms: A Survey

2019 ◽  
Vol 13 ◽  
Author(s):  
Rohit Kumar Sachan ◽  
Dharmender Singh Kushwaha
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Water Cycle ◽  
Search Algorithm ◽  
Theoretical Perspective ◽  
Gravitational Search Algorithm ◽  
Natural Phenomenon ◽  
Flower Pollination Algorithm ◽  
Shuffled Frog Leaping ◽  
Nature Inspired Algorithms

Background: Nature-Inspired Algorithms (NIAs) are the most efficient way to solve advanced engineering and real-world optimization problems. Since the last few decades, various researchers have proposed an immense number of NIAs. These NIAs get inspiration from natural phenomenon. A young researcher attempting to undertake or solve a problem using NIAs is bogged down by a plethora of proposals that exist today. Not every algorithm is suited for all kinds of problem. Some scores over others. Objective: This paper presents a comprehensive study of seven NIAs, which have new and unique inspirations. This study shall useful to easily understand the fundamentals of NIAs for any new entrant. Conclusion: Here, we classify the NIAs as natural evolution based, swarm intelligence based, biological based, science based and others. In this survey, well-establish and relatively new NIAs, namely- Shuffled Frog Leaping Algorithm (SFLA), Firefly Algorithm (FA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), Water Cycle Algorithm (WCA), Jaya Algorithm and Anti-Predatory NIA (APNIA), have been studied. This study presents a theoretical perspective of NIAs in a simplified form based on its source of inspiration, mathematical formulations, control parameters, features, variants and area of application, where these algorithms have been successfully applied.

A Comparative Study of Firefly Algorithm and Shuffled Frog-Leaping Algorithm for Constrained Free and Manufacturing Optimization Problems

2013 ◽  
Vol 457-458 ◽  
pp. 618-623
Author(s):  
Pasura Aungkulanon ◽  
Isaree Srikun ◽  
Lakkana Ruekkasem
Keyword(s):  
Manufacturing Process ◽  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Finite Sequence ◽  
Shuffled Frog Leaping Algorithm ◽  
Industrial Systems ◽  
Manufacturing Optimization ◽  
Mean And Variance ◽  
Shuffled Frog Leaping

Manufacturing process problems in industrial systems are currently large and complicated. The effective methods for solving these problems using a finite sequence of instructions can be classified into two groups; optimization and meta-heuristic algorithms. In this paper, a well-known meta-heuristic approach called Firefly Algorithm was used to compare with Shuffled Frog-leaping Algorithm. All algorithms were implemented and analyzed with manufacturing process problems under different conditions, which consist of single, multi-peak and curved ridge optimization. The results from both methods revealed that Firefly Algorithm seemed to be better in terms of the mean and variance of process yields including design points to achieve the final solution.

scholarly journals NEW HEURISTIC ALGORITHMS FOR ROLLING AIR FRAME AERODYNAMIC PARAMETERS ESTIMATION

Aviation ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Ayham Mohamad ◽  
Jalal Karimi ◽  
Alireza Naderi
Keyword(s):  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Parameters Estimation ◽  
Modified Particle Swarm Optimization ◽  
Smoothing Filter ◽  
Aerodynamic Parameters ◽  
New Strategies ◽  
Better Than

In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.

scholarly journals Automatic Design of Heuristic Algorithms for Binary Optimization Problems

2021 ◽  
Author(s):  
Marcelo de Souza
Keyword(s):  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Algorithm Design ◽  
Automatic Design ◽  
Binary Optimization ◽  
Automatic Algorithm ◽  
Better Than ◽  
Design Techniques

In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

scholarly journals Delayed improvement local search

2021 ◽  
Author(s):  
Heber F. Amaral ◽  
Sebastián Urrutia ◽  
Lars M. Hvattum
Keyword(s):  
Local Search ◽  
Heuristic Algorithms ◽  
Travelling Salesman Problem ◽  
Computation Time ◽  
The Novel ◽  
Local Optima ◽  
New Strategy ◽  
Max Cut Problem ◽  
Novel Method ◽  
Similar Solutions

AbstractLocal search is a fundamental tool in the development of heuristic algorithms. A neighborhood operator takes a current solution and returns a set of similar solutions, denoted as neighbors. In best improvement local search, the best of the neighboring solutions replaces the current solution in each iteration. On the other hand, in first improvement local search, the neighborhood is only explored until any improving solution is found, which then replaces the current solution. In this work we propose a new strategy for local search that attempts to avoid low-quality local optima by selecting in each iteration the improving neighbor that has the fewest possible attributes in common with local optima. To this end, it uses inequalities previously used as optimality cuts in the context of integer linear programming. The novel method, referred to as delayed improvement local search, is implemented and evaluated using the travelling salesman problem with the 2-opt neighborhood and the max-cut problem with the 1-flip neighborhood as test cases. Computational results show that the new strategy, while slower, obtains better local optima compared to the traditional local search strategies. The comparison is favourable to the new strategy in experiments with fixed computation time or with a fixed target.

scholarly journals Secure the IoT Networks as Epidemic Containment Game

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 156
Author(s):  
Juntao Zhu ◽  
Hong Ding ◽  
Yuchen Tao ◽  
Zhen Wang ◽  
Lanping Yu
Keyword(s):  
Heuristic Algorithms ◽  
Heuristic Method ◽  
Exact Algorithms ◽  
Epidemic Threshold ◽  
Solution Quality ◽  
Sis Model ◽  
Linear Programming Formulation ◽  
Iot Devices ◽  
General Graphs ◽  
The Internet Of Things

The spread of a computer virus among the Internet of Things (IoT) devices can be modeled as an Epidemic Containment (EC) game, where each owner decides the strategy, e.g., installing anti-virus software, to maximize his utility against the susceptible-infected-susceptible (SIS) model of the epidemics on graphs. The EC game’s canonical solution concepts are the Minimum/Maximum Nash Equilibria (MinNE/MaxNE). However, computing the exact MinNE/MaxNE is NP-hard, and only several heuristic algorithms are proposed to approximate the MinNE/MaxNE. To calculate the exact MinNE/MaxNE, we provide a thorough analysis of some special graphs and propose scalable and exact algorithms for general graphs. Especially, our contributions are four-fold. First, we analytically give the MinNE/MaxNE for EC on special graphs based on spectral radius. Second, we provide an integer linear programming formulation (ILP) to determine MinNE/MaxNE for the general graphs with the small epidemic threshold. Third, we propose a branch-and-bound (BnB) framework to compute the exact MinNE/MaxNE in the general graphs with several heuristic methods to branch the variables. Fourth, we adopt NetShiled (NetS) method to approximate the MinNE to improve the scalability. Extensive experiments demonstrate that our BnB algorithm can outperform the naive enumeration method in scalability, and the NetS can improve the scalability significantly and outperform the previous heuristic method in solution quality.

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