Imperialist Competitive Algorithm with Updated Assimilation for the Solution of Real Valued Optimization Problems

2018 ◽  
Vol 27 (02) ◽  
pp. 1850005
Author(s):  
Zhavat Sherinov ◽  
Ahmet Ünveren ◽  
Adnan Acan
Keyword(s):  
Euclidean Distance ◽  
Optimization Problems ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Imperialist Competitive Algorithm ◽  
Benchmark Problems ◽  
Pearson Correlation Coefficient ◽  
Competitive Algorithm ◽  
Imperialistic Competitive Algorithm ◽  
Better Than

In this paper, an improved imperialistic competitive algorithm is presented for real-valued optimization problems. A new method is introduced for the movement of colonies towards their imperialist, which is called assimilation. The proposed method uses Euclidean distance along with Pearson correlation coefficient as an operator for assimilating colonies with respect to their imperialists. Applications of the proposed algorithm to classical and recently published hard benchmark problems, and statistical analysis associated with the corresponding experimental results illustrated that the achieved success is significantly better than a number of state-of-the art methods.

Optimization of Cold-Formed Channel Sections Using Imperialist Competitive Algorithm

2012 ◽  
Vol 166-169 ◽  
pp. 493-496
Author(s):  
Roya Kohandel ◽  
Behzad Abdi ◽  
Poi Ngian Shek ◽  
M.Md. Tahir ◽  
Ahmad Beng Hong Kueh
Keyword(s):  
Sequential Quadratic Programming ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Computational Method ◽  
Compression Force ◽  
Sqp Method ◽  
Competitive Algorithm ◽  
Different Types ◽  
Formed Channel ◽  
Good Agreement

The Imperialist Competitive Algorithm (ICA) is a novel computational method based on the concept of socio-political motivated strategy, which is usually used to solve different types of optimization problems. This paper presents the optimization of cold-formed channel section subjected to axial compression force utilizing the ICA method. The results are then compared to the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) algorithm for validation purpose. The results obtained from the ICA method is in good agreement with the GA and SQP method in terms of weight but slightly different in the geometry shape.

Self-adaptively Commensal Learning-based Jaya Algorithm with Multi-populations and its Application

2021 ◽  
Author(s):  
Zuanjia Xie ◽  
Chunliang Zhang ◽  
Haibin Ouyang ◽  
Steven Li ◽  
Liqun Gao
Keyword(s):  
Learning Strategy ◽  
Optimization Problems ◽  
State Of The Art ◽  
Global Optimum ◽  
Benchmark Problems ◽  
Series System ◽  
Jaya Algorithm ◽  
Roulette Wheel Selection ◽  
Roulette Wheel ◽  
Distribution Scheme

Abstract Jaya algorithm is an advanced optimization algorithm, which has been applied to many real-world optimization problems. Jaya algorithm has better performance in some optimization field. However, Jaya algorithm exploration capability is not better. In order to enhance exploration capability of the Jaya algorithm, a self-adaptively commensal learning-based Jaya algorithm with multi-populations (Jaya-SCLMP) is presented in this paper. In Jaya-SCLMP, a commensal learning strategy is used to increase the probability of finding the global optimum, in which the person history best and worst information is used to explore new solution area. Moreover, a multi-populations strategy based on Gaussian distribution scheme and learning dictionary is utilized to enhance the exploration capability, meanwhile every sub-population employed three Gaussian distributions at each generation, roulette wheel selection is employed to choose a scheme based on learning dictionary. The performance of Jaya-SCLMP is evaluated based on 28 CEC 2013 unconstrained benchmark problems. In addition, three reliability problems, i.e. complex (bridge) system, series system and series-parallel system are selected. Compared with several Jaya variants and several state-of-the-art other algorithms, the experimental results reveal that Jaya-SCLMP is effective.

scholarly journals Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity Criterion of Neighboring Objects

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Maryam Houtinezhad ◽  
Hamid Reza Ghaffary
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Imperialist Competitive Algorithm ◽  
Cosine Similarity ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Competitive Algorithm ◽  
Combined Algorithm

The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

scholarly journals An imperialist competitive algorithm for the winner determination problem in combinatorial auction

2015 ◽  
Vol 4 (1) ◽  
pp. 19-30
Author(s):  
Reza Mostafavi ◽  
Seyed Naser Razavi ◽  
Mohammad Ali Balafar
Keyword(s):  
Imperialist Competitive Algorithm ◽  
Combinatorial Auction ◽  
Benchmark Problems ◽  
Winner Determination Problem ◽  
Winner Determination ◽  
Equilibrium Search ◽  
Competitive Algorithm ◽  
Complete Problems ◽  
Np Complete ◽  
Search Approach

Winner Determination problem (WDP) in combinatorial auction is an NP-complete problem. The NP-complete problems are often solved by using heuristic methods and approximation algorithms. This paper presents an imperialist competitive algorithm (ICA) for solving winner determination problem. Combinatorial auction (CA) is an auction that auctioneer considers many goods for sale and the bidder bids on the bundle of items. In this type of auction, the goal is finding winning bids that maximize the auctioneer’s income under the constraint that each item can be allocated to at most one bidder. To demonstrate, the postulated algorithm is applied over various benchmark problems. The ICA offers competitive results and finds good-quality solution in compare to genetic algorithm (GA), Memetic algorithm (MA), Nash equilibrium search approach (NESA) and Tabu search.

Evaluation on the performance of MM/PBSA in nucleic acid-protein systems

2021 ◽  
Author(s):  
Yuan-Qiang Chen ◽  
Yan-Jing Sheng ◽  
Hong-Ming Ding ◽  
Yu-Qiang Ma
Keyword(s):  
Nucleic Acid ◽  
Binding Energy ◽  
Molecular Mechanics ◽  
Pearson Correlation ◽  
Electrostatic Energy ◽  
Pearson Correlation Coefficient ◽  
Acid Protein ◽  
Poisson Boltzmann ◽  
Protein Nucleic Acid ◽  
Better Than

Abstract The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method has been widely used in predicting the binding affinity among the ligands, the proteins and the nucleic acids. However, the accuracy of the predicted binding energy by the standard MM/PBSA is not always good, especially in highly charged systems. In this work, we take the protein-nucleic acid complexes as an example, and showed that the use of screening electrostatic energy (instead of coulomb electrostatic energy) in molecular mechanics can greatly improve the performance of MM/PBSA. In particular, the Pearson correlation coefficient of dataset II in the modified MM/PBSA (i.e., screening MM/PBSA) is about 0.52, much better than that (<0.33) in the standard MM/PBSA. Further, we also evaluate the effect of the solute dielectric constant and the salt concentration on the performance of the screening MM/PBSA. The present study highlights the potential power of the screening MM/PBSA for predicting the binding energy in highly charged bio-systems.

Data Analysis by Combining the Modified K-Means and Imperialist Competitive Algorithm

2014 ◽  
Vol 70 (5) ◽  
Author(s):  
Mohammad Babrdelbonb ◽  
Siti Zaiton Mohd Hashim Mohd Hashim ◽  
Nor Erne Nazira Bazin
Keyword(s):  
Political Development ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Imperialist Competitive Algorithm ◽  
Alternative Methods ◽  
Evolutionary Computations ◽  
Suggested Approach ◽  
Standard Data ◽  
Competitive Algorithm ◽  
Optimum Solution

Data Clustering is one of the most used methods of data mining. The k-means Clustering Approach is one of the main algorithms in the literature of Pattern Recognition and Data Machine Learning which it very popular because of its simple application and high operational speed. But some obstacles such as the adherence of results to initial cluster centers or the risk of getting trapped  into local optimality hinders its performance. In this paper, inspired by the Imperialist Competitive Algorithm based on the k-means method, a new approach is developed, in which cluster centers are selected and computed appropriately. The Imperialist Competitive Algorithm (ICA) is a method in the field of evolutionary computations, trying to find the optimum solution for diverse optimization problems. The underlying traits of this algorithm are taken from the evolutionary process of social, economic and political development of countries so that by partly mathematical modeling of this process some operators are obtained in regular algorithmic forms. The investigated results of the suggested   approach over using standard data sets and comparing it with alternative methods in the literature reveals out that the proposed algorithm outperforms the k-means algorithm and other candidate algorithms in the pool.  

scholarly journals AN INTRODUCTION OF KRILL HERD ALGORITHM FOR ENGINEERING OPTIMIZATION

2015 ◽  
Vol 22 (3) ◽  
pp. 302-310 ◽  
Author(s):  
Amir H. GANDOMI ◽  
Amir H. ALAVI
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
State Of The Art ◽  
Engineering Optimization ◽  
Design Problems ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Metaheuristic Optimization Algorithm ◽  
Engineering Optimization Problems ◽  
Better Than

A new metaheuristic optimization algorithm, called Krill Herd (KH), has been recently proposed by Gandomi and Alavi (2012). In this study, KH is introduced for solving engineering optimization problems. For more verification, KH is applied to six design problems reported in the literature. Further, the performance of the KH algorithm is com­pared with that of various algorithms representative of the state-of-the-art in the area. The comparisons show that the results obtained by KH are better than the best solutions obtained by the existing methods.

scholarly journals Using Derived Kernel as a new Method for Recognition a Similarity Learning.

2020 ◽  
Vol 9 (3) ◽  
pp. 1974-1980
Keyword(s):  
Significant Relationship ◽  
State Of The Art ◽  
Pearson Correlation ◽  
Research Work ◽  
Entropy Measure ◽  
Pearson Correlation Coefficient ◽  
Similarity Learning ◽  
Entropy Measures ◽  
A New Technique ◽  
Effective Models

A new technique for feature withdrawal by neural response is going to be familiarized in this research work by merging an entropy measure with Squared Pearson correlation Coefficient (SPCC) method. The process of choosing effective models on the basis of entropy measures was proposed further to enhance the ability to select templates. For more accurate similarity measure we used the statistical significant relationship between functions. The research illustrate that the proposed method is proficiently compared with the state-of-the-art methods.

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