scholarly journals A Multiobjective Brain Storm Optimization Algorithm Based on Decomposition

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Cai Dai ◽  
Xiujuan Lei
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
State Of The Art ◽  
Selection Strategy ◽  
Test Problems ◽  
Search Efficiency ◽  
Exploration And Exploitation ◽  
Adaptive Selection ◽  
Brain Storm Optimization ◽  
Cluster Solutions

Brain storm optimization (BSO) algorithm is a simple and effective evolutionary algorithm. Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO/D compares with three efficient state-of-the-art algorithms, e.g., NSGAII and MOEA/D, on twenty-two test problems. The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.

scholarly journals An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 617
Author(s):  
P. Arun Mozhi Devan ◽  
Fawnizu Azmadi Hussin ◽  
Rosdiazli B. Ibrahim ◽  
Kishore Bingi ◽  
M. Nagarajapandian ◽  
...  
Keyword(s):  
Real Time ◽  
Optimization Algorithm ◽  
Time Pressure ◽  
Optimization Technique ◽  
Test Problems ◽  
Trigonometric Functions ◽  
Exploration And Exploitation ◽  
Multiple Dimensions ◽  
Process Plant ◽  
Sine Cosine Algorithm

This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase the effectiveness of ATOA. Furthermore, the different variants of the ATOA optimization technique are used to obtain the controller parameters for the real-time pressure process plant to investigate its performance. The obtained results have shown a remarkable performance improvement compared with the existing algorithms.

scholarly journals A Novel Multi-Objective Artificial Bee Colony Algorithm for the QoS Based Wireless Route Optimization Problem

2012 ◽  
Vol 6-7 ◽  
pp. 445-451
Author(s):  
Chang Sheng Zhang ◽  
Ming Kang Ren ◽  
Bin Zhang
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Artificial Bee Colony ◽  
Route Optimization ◽  
Test Problems ◽  
Nsga Ii ◽  
Solution Quality ◽  
Artificial Bee Colony Optimization ◽  
Multi Objective ◽  
Bee Colony

In this paper, an efficient multi-objective artificial bee colony optimization algorithm based on Pareto dominance called PC_MOABC is proposed to tackle the QoS based route optimization problem. The concepts of Pareto strength and crowding distance are introduced into this algorithm, and are combined together effectively to improve the algorithm’s efficiency and generate a set of evenly distributed solutions. The proposed algorithm was evaluated on a set of different scale test problems and compared with the recently proposed popular NSGA-II based multi-objective optimization algorithm. The experimental results reveal very encouraging results in terms of the solution quality and the processing time required.

scholarly journals An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Tongyi Zheng ◽  
Weili Luo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Learning Strategy ◽  
State Of The Art ◽  
Metaheuristic Algorithms ◽  
Competitive Performance ◽  
Exploration And Exploitation ◽  
Global Optimization Algorithm ◽  
Opposition Based Learning ◽  
Searching Ability

Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.

scholarly journals An Improved Squirrel Search Algorithm for Optimization

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-31 ◽  
Author(s):  
Tongyi Zheng ◽  
Weili Luo
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Search Algorithm ◽  
Cloud Model ◽  
Adaptive Strategy ◽  
Selection Strategy ◽  
Test Results ◽  
Exploration And Exploitation ◽  
Flying Squirrel ◽  
Flying Squirrels

Squirrel search algorithm (SSA) is a new biological-inspired optimization algorithm, which has been proved to be more effective for solving unimodal, multimodal, and multidimensional optimization problems. However, similar to other swarm intelligence-based algorithms, SSA also has its own disadvantages. In order to get better global convergence ability, an improved version of SSA called ISSA is proposed in this paper. Firstly, an adaptive strategy of predator presence probability is proposed to balance the exploration and exploitation capabilities of the algorithm. Secondly, a normal cloud model is introduced to describe the randomness and fuzziness of the foraging behavior of flying squirrels. Thirdly, a selection strategy between successive positions is incorporated to preserve the best position of flying squirrel individuals. Finally, in order to enhance the local search ability of the algorithm, a dimensional search enhancement strategy is utilized. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are used to test the global search ability of the proposed ISSA. Experimental test results indicate that ISSA provides competitive performance compared with the basic SSA and other four well-known state-of-the-art optimization algorithms.

scholarly journals An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yufang Qin ◽  
Junzhong Ji ◽  
Chunnian Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Pareto Front ◽  
Optimization Problem ◽  
State Of The Art ◽  
Early Stage ◽  
Test Problems ◽  
Multiobjective Optimization Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.

scholarly journals A Cuckoo Search Algorithm for Multimodal Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Cuevas ◽  
Adolfo Reyna-Orta
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Selection Strategy ◽  
Test Problems ◽  
Multimodal Optimization ◽  
Local Optima ◽  
Fitness Value

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.

scholarly journals Improved grey wolf algorithm for optimization problems

2021 ◽  
Vol 22 (3) ◽  
pp. 1573
Author(s):  
Hafiz Maaz Asgher ◽  
Yana Mazwin Mohmad Hassim ◽  
Rozaida Ghazali ◽  
Muhammad Aamir
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Research Work ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Grey Wolf ◽  
Exploration And Exploitation ◽  
Grey Wolf Optimization ◽  
Whale Optimization

The grey wolf optimization (GWO) is a nature inspired and meta-heuristic algorithm, it has successfully solved many optimization problems and give better solution as compare to other algorithms. However, due to its poor exploration capability, it has imbalance relation between exploration and exploitation. Therefore, in this research work, the poor exploration part of GWO was improved through hybrid with whale optimization algorithm (WOA) exploration. The proposed grey wolf whale optimization algorithm (GWWOA) was evaluated on five unimodal and five multimodal benchmark functions. The results shows that GWWOA offered better exploration ability and able to solve the optimization problem and give better solution in search space. Additionally, GWWOA results were well balanced and gave the most optimal in search space as compare to the standard GWO and WOA algorithms.

MSA: An Application of Brain Storm Optimization Algorithm

2020 ◽  
Author(s):  
Pujari Jeevana Jyothi ◽  
Karteeka Pavan K ◽  
S M Raiyyan ◽  
T Rajasekhar
Keyword(s):  
Optimization Algorithm ◽  
Brain Storm Optimization

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

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