scholarly journals Novel Optimization Algorithm Inspired by Camel Traveling Behavior

2016 ◽  
Vol 12 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Mohammed Ibrahim ◽  
Ramzy Ali
Keyword(s):  
Optimization Algorithm ◽  
Optimal Solution ◽  
Test Functions ◽  
Desert Environment ◽  
Benchmark Test ◽  
Benchmark Test Functions

This article presents a novel optimization algorithm inspired by camel traveling behavior that called Camel algorithm (CA). Camel is one of the extraordinary animals with many distinguish characters that allow it to withstand the severer desert environment. The Camel algorithm used to find the optimal solution for several different benchmark test functions. The results of CA and the results of GA and PSO algorithms are experimentally compared. The results indicate that the promising search ability of camel algorithm is useful, produce good results and outperform the others for different test functions.

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

An Improved Novel Global Harmony Search Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2169-2172
Author(s):  
Zhi Kong ◽  
Guo Dong Zhang ◽  
Li Fu Wang
Keyword(s):  
Convergence Rate ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Global Optimum ◽  
Harmony Search Algorithm ◽  
Test Functions ◽  
Benchmark Test ◽  
Novel Method ◽  
Benchmark Test Functions

This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

scholarly journals Parametric Study of Dual-particle Swarm Optimisation-modified Adaptive Bats Sonar Algorithm on Multi-objective Benchmark Test Functions

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 72-80
Author(s):  
Nafrizuan Mat Yahya ◽  
Nur Atikah Nor’Azlan ◽  
M Osman Tokhi
Keyword(s):  
Parametric Study ◽  
Pareto Front ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Test Functions ◽  
Benchmark Test ◽  
Multi Objective ◽  
Searching Strategy ◽  
Particle Swarm Optimisation Algorithm ◽  
Benchmark Test Functions

An integrated algorithm for solving multi-objective optimisation problems using a dual- level searching approach is presented. The proposed algorithm named as dual-particle swarm optimisation-modified adaptive bats sonar algorithm (D-PSO-MABSA) where the concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is combined with the established particle swarm optimisation algorithm. The proposed algorithm combines the advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handling the complexity of multi-objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi- objective optimisation benchmark test functions. The acquired results show that the proposed algorithm performs well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving multi-objective optimisation problems.

scholarly journals Local search algorithms based on benchmark test functions problem

2020 ◽  
Vol 9 (3) ◽  
pp. 529
Author(s):  
Atheer Bassel ◽  
Hussein M. Haglan ◽  
Akeel Sh. Mahmoud
Keyword(s):  
Local Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Optimization Techniques ◽  
Engineering Optimization ◽  
Local Search Algorithm ◽  
Test Functions ◽  
Local Search Algorithms ◽  
Benchmark Test ◽  
Benchmark Test Functions

<p>Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.</p>

scholarly journals Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1418
Author(s):  
Olympia Roeva ◽  
Dafina Zoteva ◽  
Velislava Lyubenova
Keyword(s):  
Escherichia Coli ◽  
Parameter Identification ◽  
Real World ◽  
Hybrid Algorithm ◽  
Hybrid Algorithms ◽  
Test Functions ◽  
Biologically Inspired ◽  
Benchmark Test ◽  
Cultivation Process ◽  
Benchmark Test Functions

In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behavior of honey bees, and GA, based on the mechanics of nature selection, are among the most efficient biologically inspired population-based algorithms. The performance of the proposed ABC-GA hybrid algorithm is examined, including classic benchmark test functions. To demonstrate the effectiveness of ABC-GA for a real-world problem, parameter identification of an Escherichia coli MC4110 fed-batch cultivation process model is considered. The computational results of the designed algorithm are compared to the results of different hybridized biologically inspired techniques (ant colony optimization (ACO) and firefly algorithm (FA))—hybrid algorithms as ACO-GA, GA-ACO and ACO-FA. The algorithms are applied to the same problems—a set of benchmark test functions and the real nonlinear optimization problem. Taking into account the overall searchability and computational efficiency, the results clearly show that the proposed ABC–GA algorithm outperforms the considered hybrid algorithms.

scholarly journals A Cuckoo Search Algorithm Using Improved Beta Distributing and Its Application in the Process of EDM

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 916
Author(s):  
Dili Shen ◽  
Wuyi Ming ◽  
Xinggui Ren ◽  
Zhuobin Xie ◽  
Yong Zhang ◽  
...  
Keyword(s):  
Random Walk ◽  
Beta Distribution ◽  
Cuckoo Search ◽  
Scale Factor ◽  
Test Functions ◽  
Benchmark Test ◽  
Iteration Number ◽  
Benchmark Test Functions ◽  
Single Objective ◽  
Better Than

Lévy flights random walk is one of key parts in the cuckoo search (CS) algorithm to update individuals. The standard CS algorithm adopts the constant scale factor for this random walk. This paper proposed an improved beta distribution cuckoo search (IBCS) for this factor in the CS algorithm. In terms of local characteristics, the proposed algorithm makes the scale factor of the step size in Lévy flights showing beta distribution in the evolutionary process. In terms of the overall situation, the scale factor shows the exponential decay trend in the process. The proposed algorithm makes full use of the advantages of the two improvement strategies. The test results show that the proposed strategy is better than the standard CS algorithm or others improved by a single improvement strategy, such as improved CS (ICS) and beta distribution CS (BCS). For the six benchmark test functions of 30 dimensions, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 3.67, 2.67, 1.5, and 1.17, respectively. For the six benchmark test functions of 50 dimensions, moreover, the average rankings of the CS, ICS, BCS, and IBCS algorithms are 2.83, 2.5, 1.67, and 1.0, respectively. Confirmed by our case study, the performance of the ABCS algorithm was better than that of standard CS, ICS or BCS algorithms in the process of EDM. For example, under the single-objective optimization convergence of MRR, the iteration number (13 iterations) of the CS algorithm for the input process parameters, such as discharge current, pulse-on time, pulse-off time, and servo voltage, was twice that (6 iterations) of the IBCS algorithm. Similar, the iteration number (17 iterations) of BCS algorithm for these parameters was twice that (8 iterations) of the IBCS algorithm under the single-objective optimization convergence of Ra. Therefore, it strengthens the CS algorithm’s accuracy and convergence speed.

scholarly journals Chaotic Immune Symbiotic Organisms Search Algorithm for Solving Optimisation Problem

2018 ◽  
Vol 7 (3.15) ◽  
pp. 73 ◽  
Author(s):  
Mohamad Khairuzzaman Mohamad Zamani ◽  
Ismail Musirin ◽  
Saiful Izwan Suliman ◽  
Sharifah Azma Syed Mustaffa
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Optimal Solutions ◽  
Test Functions ◽  
High Quality ◽  
Symbiotic Organisms Search ◽  
Benchmark Test Functions ◽  
Symbiotic Organisms ◽  
Optimisation Techniques ◽  
Optimisation Algorithms

Achieving an optimal solution is very crucial while solving a problem. To achieve the optimality required, optimisation techniques can be implemented while solving the problem. The presence of classical optimisation techniques has enabled an optimal solution to be obtained. However, as the complexity of the optimisation problem increased, classical optimisation techniques faced difficulties in providing optimal solutions. Heuristics-based algorithms were introduced to counter the problem faced by classical optimisation techniques. Good performance of these heuristics-based algorithm has been implied through various implementation in solving optimisation problems. Despite the performance of these algorithms, the flaws of these algorithms hinder them from producing high-quality results. To mitigate the problem, this paper presents the development of Chaotic Immune Symbiotic Organisms Search algorithm which was inspired by the element of diversification as well as the increased capability of exploration. The performance of the proposed algorithm has been tested by solving several benchmark test functions. A comparative study was also conducted with respect to several other existing optimisation algorithms resulted in the superiority of the proposed algorithm in providing high-quality solutions.  

scholarly journals A Novel Quantum-Behaved Bat Algorithm with Mean Best Position Directed for Numerical Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Binglian Zhu ◽  
Wenyong Zhu ◽  
Zijuan Liu ◽  
Qingyan Duan ◽  
Long Cao
Keyword(s):  
Numerical Optimization ◽  
Early Stage ◽  
Optimal Solution ◽  
Bat Algorithm ◽  
Accurate Solution ◽  
Test Functions ◽  
Local Optimal Solution ◽  
Quantum Behavior ◽  
The Mean ◽  
Benchmark Test Functions

This paper proposes a novel quantum-behaved bat algorithm with the direction of mean best position (QMBA). In QMBA, the position of each bat is mainly updated by the current optimal solution in the early stage of searching and in the late search it also depends on the mean best position which can enhance the convergence speed of the algorithm. During the process of searching, quantum behavior of bats is introduced which is beneficial to jump out of local optimal solution and make the quantum-behaved bats not easily fall into local optimal solution, and it has better ability to adapt complex environment. Meanwhile, QMBA makes good use of statistical information of best position which bats had experienced to generate better quality solutions. This approach not only inherits the characteristic of quick convergence, simplicity, and easy implementation of original bat algorithm, but also increases the diversity of population and improves the accuracy of solution. Twenty-four benchmark test functions are tested and compared with other variant bat algorithms for numerical optimization the simulation results show that this approach is simple and efficient and can achieve a more accurate solution.

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