scholarly journals Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

Algorithms ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 358
Author(s):  
Robertas Damaševičius ◽  
Rytis Maskeliūnas
Keyword(s):  
Hybrid Algorithm ◽  
Probability Function ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Hybrid Scheme ◽  
Krill Herd ◽  
Heuristic Technique ◽  
Truss Design Problem ◽  
Logistic Probability

This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

scholarly journals THE USE OF HEURISTIC ALGORITHMS TO OPTIMIZE THE TRANSPORT ISSUES ON THE EXAMPLE OF MUNICIPAL SERVICES COMPANIES

2014 ◽  
Vol 29 (1) ◽  
pp. 27-36 ◽  
Author(s):  
Mariusz Izdebski
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Selection Process ◽  
Random Search ◽  
Municipal Services ◽  
Random Search Algorithm ◽  
Two Stages

In this article the main optimization problems in the municipal services companies were presented. These problems concern the issue of vehicle routing. The mathematical models of these problems were described. The function of criterion and the conditions on designating the vehicle routing were defined. In this paper the hybrid algorithm solving the presented problems was proposed. The hybrid algorithm consists of two heuristic algorithms: the ant and the genetic algorithm. In this paper the stages of constructing of the hybrid algorithm were presented. A structure of the data processed by the algorithm, a function of adaptation, a selection of chromosomes, a crossover, a mutation and an inversion were characterized. A structure of the data was presented as string of natural numbers. In selection process the roulette method was used and in the crossover process the operator PMX was presented. This algorithm was verified in programming language C #. The process of verification was divided into two stages. In the first stage the best parameters of the hybrid algorithm were designated. In the second stage the algorithm was started with these parameters and the result was compared with the random search algorithm. The random search algorithm generates 2000 routes and the best result is compared with the hybrid algorithm.

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.

scholarly journals A hybridization of differential evolution and monarch butterfly optimization for solving systems of nonlinear equations

2018 ◽  
Vol 6 (3) ◽  
pp. 354-367 ◽  
Author(s):  
Abdelmonem M. Ibrahim ◽  
Mohamed A. Tawhid
Keyword(s):  
Nonlinear Systems ◽  
Differential Evolution ◽  
Unconstrained Optimization ◽  
Nonlinear Equations ◽  
Hybrid Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Monarch Butterfly ◽  
Monarch Butterfly Optimization ◽  
Unconstrained Optimization Problems

Abstract In this study, we propose a new hybrid algorithm consisting of two meta-heuristic algorithms; Differential Evolution (DE) and the Monarch Butterfly Optimization (MBO). This hybrid is called DEMBO. Both of the meta-heuristic algorithms are typically used to solve nonlinear systems and unconstrained optimization problems. DE is a common metaheuristic algorithm that searches large areas of candidate space. Unfortunately, it often requires more significant numbers of function evaluations to get the optimal solution. As for MBO, it is known for its time-consuming fitness functions, but it traps at the local minima. In order to overcome all of these disadvantages, we combine the DE with MBO and propose DEMBO which can obtain the optimal solutions for the majority of nonlinear systems as well as unconstrained optimization problems. We apply our proposed algorithm, DEMBO, on nine different, unconstrained optimization problems and eight well-known nonlinear systems. Our results, when compared with other existing algorithms in the literature, demonstrate that DEMBO gives the best results for the majority of the nonlinear systems and unconstrained optimization problems. As such, the experimental results demonstrate the efficiency of our hybrid algorithm in comparison to the known algorithms. Highlights This paper proposes a new hybridization of differential evolution and monarch butterfly optimization. Solve system of nonlinear equations and unconstrained optimization problem. The efficiency and effectiveness of our algorithm are provided. Experimental results prove the superiority of our algorithm over the state-of-the-arts.

scholarly journals Critical analysis: bat algorithm-based investigation and application on several domains

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahla U. Umar ◽  
Tarik A. Rashid
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Survey Study ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Enhanced Particle Swarm Optimization ◽  
Modified Bat Algorithm

Purpose The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms. Design/methodology/approach Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm). Findings Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

Implementacija algoritma kolonije mrava za optimalnu rekonfiguraciju distributivne mreže

2020 ◽  
Vol 22 (1-2) ◽  
pp. 50-57
Author(s):  
◽  
Nikola Rajaković
Keyword(s):  
Combinatorial Optimization ◽  
Distribution Systems ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Population Based ◽  
Active Power ◽  
Search Technique ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
Heuristic Technique

The paper analyzes the possibility of application of Ant Colony Optimization (ACO) algorithm for reconfiguration of distribution network with the aim of active power minimiza- tion. ACO is a population-based meta-heuristic technique used to solve different combinatorial optimization problems. The search technique is inspired by the behaviour of ant colonies in nature. The efficiency of the proposed algorithm is demonstrated on IEEE 33-bus and IEEE 69-bus test distribution systems. Also, the results obtained by using ACO algorithm are compared to the results achievable by other heuristic and meta-heuristic algorithms.

Hybridization of Firefly and Water Wave Algorithm for Solving Reactive Power Problem

2015 ◽  
Vol 21 ◽  
pp. 165-171 ◽  
Author(s):  
K. Lenin ◽  
B. Ravindhranath Reddy ◽  
M. Suryakalavathi
Keyword(s):  
Water Wave ◽  
Power Loss ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Reactive Power ◽  
Optimization Problems ◽  
Test System ◽  
Heuristic Technique ◽  
Simulation Results ◽  
Power Problem

In this paper, a hybrid algorithm as the combination of Firefly and Water Wave algorithm (FWW) has been proposed to solve the Reactive power problem. The firefly algorithm is a meta-heuristic technique which is widely used for solving the optimization problems. The water wave optimization algorithm is also a nature inspired based algorithm. Both algorithms collectively improved the performance of search. The water wave algorithm is work on the combinatorial optimization and utilized as application of firefly algorithm. Hence we merge these two algorithms and make a hybrid algorithm. Proposed FWW algorithm has been tested in standard IEEE 30 Bus test system and simulation results reveal the better performance of the proposed algorithm in reducing the real power loss and voltage profiles were found to be within the limits.

scholarly journals Extension of Bat Algorithm on Standard Benchmark Functions

2020 ◽  
Vol 8 (5) ◽  
pp. 802-807
Keyword(s):  
Global Optimization ◽  
Pulse Rate ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Bat Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Dynamic Pulse

Meta heuristics are superior methods of finding, producing and even modifying heuristics that are able to solve various optimization problems. All Meta-heuristic algorithms are influenced by the nature. These types of algorithms tend to mimic the behaviour of biotic components in nature and are emerging as an effective way of solving global optimization algorithms. We have reviewed that no any algorithm is best for all applications due to lack of generality (no. of parameters), non-dynamic input values. So, this paper studied BAT algorithm deeply and found weakness in terms of non-dynamic pulse rate and loudness. In order to avoid being trapped into local optima these inputs are made dynamic with inclusion of levy Flight too. Performance of this proposed Modified BAT approach is evaluated using few standard benchmark functions. For justifying the superiority of Modified BAT, its performance has been compared with standard Bat algorithm too. From simulation it is found that dynamic pulse rate and dynamic loudness improve the performance of Bat algorithm in terms of results without being stuck at local optima and is more general

scholarly journals Development of a Hybrid Algorithm for efficient Task Scheduling in Cloud Computing environment using Artificial Intelligence

2021 ◽  
Vol 16 (5) ◽  
Author(s):  
Mohammed Yousuf Uddin ◽  
Hikmat Awad Abdeljaber ◽  
Tariq Ahamed Ahanger
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Task Scheduling ◽  
Hybrid Algorithm ◽  
Virtual Machines ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Machine Scheduling ◽  
Virtual Machine Scheduling

Cloud computing is developing as a platform for next generation systems where users can pay as they use facilities of cloud computing like any other utilities. Cloud environment involves a set of virtual machines, which share the same computation facility and storage. Due to rapid rise in demand for cloud computing services several algorithms are being developed and experimented by the researchers in order to enhance the task scheduling process of the machines thereby offering optimal solution to the users by which the users can process the maximum number of tasks through minimal utilization of the resources. Task scheduling denotes a set of policies to regulate the task processed by a system. Virtual machine scheduling is essential for effective operations in distributed environment. The aim of this paper is to achieve efficient task scheduling of virtual machines, this study proposes a hybrid algorithm through integrating two prominent heuristic algorithms namely the BAT Algorithm and the Ant Colony Optimization (ACO) algorithm in order to optimize the virtual machine scheduling process. The performance evaluation of the three algorithms (BAT, ACO and Hybrid) reveal that the hybrid algorithm performs better when compared with that of the other two algorithms.

scholarly journals An Improved Flower Pollination Algorithm for Global and Local Optimization

2021 ◽  
Vol 5 (4) ◽  
pp. 461
Author(s):  
M. Iqbal Kamboh ◽  
Nazri Bin Mohd Nawi ◽  
Azizul Azhar Ramli ◽  
Fanni Sukma
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Bat Algorithm ◽  
Search Space ◽  
Flower Pollination Algorithm ◽  
Mathematical Functions ◽  
Flower Pollination ◽  
Linear Optimization Problems ◽  
Global And Local Optimization

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.

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.

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