A fuzzy imperialistic competitive algorithm for optimizing convex functions

2014 ◽  
Vol 7 (2) ◽  
pp. 192-208 ◽  
Author(s):  
Mahsan Esmaeilzadeh Tarei ◽  
Bijan Abdollahi ◽  
Mohammad Nakhaei
Keyword(s):  
Fuzzy Logic ◽  
Convex Functions ◽  
Optimization Problems ◽  
Imperialist Competitive Algorithm ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Local Optima ◽  
Content Type ◽  
Competitive Algorithm

Purpose – The purpose of this paper is to describe imperialist competitive algorithm (ICA), a novel socio-politically inspired optimization strategy for proposing a fuzzy variant of this algorithm. ICA is a meta-heuristic algorithm for dealing with different optimization tasks. The basis of the algorithm is inspired by imperialistic competition. It attempts to present the social policy of imperialisms (referred to empires) to control more countries (referred to colonies) and use their sources. If one empire loses its power, among the others making a competition to take possession of it. Design/methodology/approach – In fuzzy imperialist competitive algorithm (FICA), the colonies have a degree of belonging to their imperialists and the top imperialist, as in fuzzy logic, rather than belonging completely to just one empire therefore the colonies move toward the superior empire and their relevant empires. Simultaneously for balancing the exploration and exploitation abilities of the ICA. The algorithms are used for optimization have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. FICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing fuzzy logic on it. Findings – Therefore several solution procedures, including ICA, FICA, genetic algorithm, particle swarm optimization, tabu search and simulated annealing optimization algorithm are considered. Finally numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed fuzzy ICA for convex functions with little fluctuations. Originality/value – The proposed evolutionary algorithm, FICA, can be used in diverse areas of optimization problems where convex functions properties are appeared including, industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning (optimization techniques; fuzzy logic; convex functions).

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.

Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

2017 ◽  
Vol 11 (4) ◽  
pp. 522-540 ◽  
Author(s):  
Isham Alzoubi ◽  
Mahmoud Delavar ◽  
Farhad Mirzaei ◽  
Babak Nadjar Arrabi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Compressibility Factor ◽  
Imperialist Competitive Algorithm ◽  
Content Type ◽  
Competitive Algorithm ◽  
Land Leveling ◽  
Soil Compressibility ◽  
Artificial Neural

Purpose This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling. Design/methodology/approach Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated. Findings According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R2) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively. Originality/value A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

2018 ◽  
Vol 35 (4) ◽  
pp. 1774-1787 ◽  
Author(s):  
Katayoun Behzadafshar ◽  
Fahimeh Mohebbi ◽  
Mehran Soltani Tehrani ◽  
Mahdi Hasanipanah ◽  
Omid Tabrizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Content Type ◽  
Competitive Algorithm ◽  
Spacing Ratio ◽  
Artificial Neural

PurposeThe purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.Design/methodology/approachFor this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.FindingsAfter modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with theR2 of 0.939 was the most precise model for predicting the PPV in the present study.Originality/valueIn the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

Imperialist Competitive Algorithms with Perturbed Moves for Global Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 3135-3139 ◽  
Author(s):  
Jun Lin Lin ◽  
Chun Wei Cho ◽  
Hung Chjh Chuan
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Population Based ◽  
Experimental Results ◽  
Local Optimum ◽  
Competitive Algorithm ◽  
Competitive Algorithms

Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. Previous works have shown that ICA converges quickly but often to a local optimum. To overcome this problem, this work proposed two modifications to ICA: perturbed assimilation move and boundary bouncing. The proposed modifications were applied to ICA and tested using six well-known benchmark functions with 30 dimensions. The experimental results indicate that these two modifications significantly improve the performance of ICA on all six benchmark functions.

An Improved Differential Evolution Algorithm for Numerical Optimization Problems

2013 ◽  
Vol 415 ◽  
pp. 349-352
Author(s):  
Hong Wei Zhao ◽  
Hong Gang Xia
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Local Optimum ◽  
Local Optima ◽  
De Algorithm ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

Differential evolution (DE) is a population-based stochastic function minimizer (or maximizer), whose simple yet powerful and straightforward features make it very attractive for numerical optimization. However, DE is easy to trapped into local optima. In this paper, an improved differential evolution algorithm (IDE) proposed to speed the convergence rate of DE and enhance the global search of DE. The IDE employed a new mutation operation and modified crossover operation. The former can rapidly enhance the convergence of the MDE, and the latter can prevent the MDE from being trapped into the local optimum effectively. Besides, we dynamic adjust the scaling factor (F) and the crossover rate (CR), which is aimed at further improving algorithm performance. Based on several benchmark experiment simulations, the IDE has demonstrated stronger convergence and stability than original differential (DE) algorithm and other algorithms (PSO and JADE) that reported in recent literature.

scholarly journals Spacecraft Multiple-Impulse Trajectory Optimization Using Differential Evolution Algorithm with Combined Mutation Strategies and Boundary-Handling Schemes

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yuehe Zhu ◽  
Hua Wang ◽  
Jin Zhang
Keyword(s):  
Differential Evolution ◽  
Trajectory Optimization ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Global Optimum ◽  
Local Optimum ◽  
Local Optima ◽  
Trial Vector ◽  
Mutation Strategies

Since most spacecraft multiple-impulse trajectory optimization problems are complex multimodal problems with boundary constraint, finding the global optimal solution based on the traditional differential evolution (DE) algorithms becomes so difficult due to the deception of many local optima and the probable existence of a bias towards suboptimal solution. In order to overcome this issue and enhance the global searching ability, an improved DE algorithm with combined mutation strategies and boundary-handling schemes is proposed. In the first stage, multiple mutation strategies are utilized, and each strategy creates a mutant vector. In the second stage, multiple boundary-handling schemes are used to simultaneously address the same infeasible trial vector. Two typical spacecraft multiple-impulse trajectory optimization problems are studied and optimized using the proposed DE method. The experimental results demonstrate that the proposed DE method efficiently overcomes the problem created by the convergence to a local optimum and obtains the global optimum with a higher reliability and convergence rate compared with some other popular evolutionary methods.

scholarly journals An Improved Moth-Flame Optimization Algorithm with Adaptation Mechanism to Solve Numerical and Mechanical Engineering Problems

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1637
Author(s):  
Mohammad H. Nadimi-Shahraki ◽  
Ali Fatahi ◽  
Hoda Zamani ◽  
Seyedali Mirjalili ◽  
Laith Abualigah
Keyword(s):  
Mechanical Engineering ◽  
Optimization Problems ◽  
Population Diversity ◽  
Local Optimum ◽  
Friedman Test ◽  
Exploration And Exploitation ◽  
Adaptation Mechanism ◽  
Local Optima ◽  
Engineering Problems ◽  
Moth Flame Optimization Algorithm

Moth-flame optimization (MFO) algorithm inspired by the transverse orientation of moths toward the light source is an effective approach to solve global optimization problems. However, the MFO algorithm suffers from issues such as premature convergence, low population diversity, local optima entrapment, and imbalance between exploration and exploitation. In this study, therefore, an improved moth-flame optimization (I-MFO) algorithm is proposed to cope with canonical MFO’s issues by locating trapped moths in local optimum via defining memory for each moth. The trapped moths tend to escape from the local optima by taking advantage of the adapted wandering around search (AWAS) strategy. The efficiency of the proposed I-MFO is evaluated by CEC 2018 benchmark functions and compared against other well-known metaheuristic algorithms. Moreover, the obtained results are statistically analyzed by the Friedman test on 30, 50, and 100 dimensions. Finally, the ability of the I-MFO algorithm to find the best optimal solutions for mechanical engineering problems is evaluated with three problems from the latest test-suite CEC 2020. The experimental and statistical results demonstrate that the proposed I-MFO is significantly superior to the contender algorithms and it successfully upgrades the shortcomings of the canonical MFO.

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).

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