Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

Bernal, Emer; Castillo, Oscar; Soria, José Manuel Nieto; Valdez, Fevrier

doi:10.3390/a10010018

Cited by 44 publications

(19 citation statements)

References 30 publications

(25 reference statements)

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“…It belongs to classes of evolutionary algorithm extracted from the imperialist competition. It begins with an initial population known as countries; a country includes groups of colonies and imperialist that together from empires [55][56][57]. In reality, imperialist countries are always trying to conquer other countries to turn them into their own colonies and complete among themselves actively for colonization of other nation; a proposed evolutionary heuristic is the competition that arises between empires in the solution space.…”

Section: Imperialist Competitive Algorithm (Ica)mentioning

confidence: 99%

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Abubakar¹,

Sathasivam²,

Mansor³

et al. 2020

Preprint

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Election Algorithm (EA) is a powerful metaheuristics model motivated by phenomena of the socio-political mechanism of the presidential election conducted in many countries. EA is selected as a topic of discussion due to its capability and robustness to carry out complex problems in the random-2SAT logic program. This paper utilizes a hybridized EA assimilated with the Hopfield neural network (HNN) in carrying out random logic program (HNN-R2SATEA). The efficiency of the proposed method was compared with the existing traditional exhaustive search (HNN-R2SATES) model and the recently introduced HNN-R2SATICA model. From the result obtained, clearly proven that based on our proposed hybrid model outperformed other existing model based on the Global Minima Ratio (ZM), Mean Absolute Error (MAE), Bayesian Information Criterion (BIC) and Execution Time (ET). The expected outcome portrays that the EA algorithm outperformed the other two algorithms in doing random-kSAT logic program. The results proved the robustness, effectiveness, and compatibility of the HNN-R2SATEA model.

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Section: Imperialist Competitive Algorithm (Ica)mentioning

confidence: 99%

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Abubakar¹,

Sathasivam²,

Mansor³

et al. 2020

Preprint

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“…Firstly, the ICA algorithm was studied in the original form in order to find out how it works and what parameters are more effective regarding the results. Various designs for fuzzy systems for dynamic adjustment of the ICA parameters were proposed as well (Bernal et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Donkey and smuggler optimization algorithm: A collaborative working approach to path finding

Shamsaldin

Rashid

Agha

et al. 2019

Journal of Computational Design and Engineering

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Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these algorithms allows researchers to simulate different natural phenomena to solve various real-world problems. This paper suggests a novel algorithm called Donkey and Smuggler Optimization Algorithm (DSO). The DSO is inspired by the searching behavior of donkeys. The algorithm imitates transportation behavior such as searching and selecting routes for movement by donkeys in the actual world. Two modes are established for implementing the search behavior and route-selection in this algorithm. These are the Smuggler and Donkeys. In the Smuggler mode, all the possible paths are discovered and the shortest path is then found. In the Donkeys mode, several donkey behaviors are utilized such as Run, Face & Suicide, and Face & Support. Real world data and applications are used to test the algorithm. The experimental results consisted of two parts, firstly, we used the standard benchmark test functions to evaluate the performance of the algorithm in respect to the most popular and the state of the art algorithms. Secondly, the DSO is adapted and implemented on three real-world applications namely; traveling salesman problem, packet routing, and ambulance routing. The experimental results of DSO on these real-world problems are very promising. The results exhibit that the suggested DSO is appropriate to tackle other unfamiliar search spaces and complex problems.

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“…Tables [12][13][14][15][16][17] show the values of z, "S" means that evidence of significance is found and "N.S" refers to a case in which no evidence of significance is found. In all Tables 12-17 the result of the Evidence of the first row represents the comparison between FHS and HS, the result of the Evidence of the second row represents the comparison between FHS2 and HS, and finally the result of the Evidence of the third row is the comparison between FHS and FHS2 methods.…”

Section: Statistical Comparisonmentioning

confidence: 99%

“…The proposed method is called fuzzy harmony search algorithm (FHS) that performs an adaptation of parameters with Type-1 and interval Type-2 fuzzy logic and it is used to optimize fuzzy tracking controllers so that they follow desired trajectories for benchmark control problems. Other metaheuristics that have been used to solve optimization problems based on fuzzy logic are [10][11][12][13][14][15]. In previous works this HS algorithm has been used for different optimization problems, as can be found in [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

2017

Self Cite

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This article focuses on the dynamic parameter adaptation in the harmony search algorithm using Type-1 and interval Type-2 fuzzy logic. In particular, this work focuses on the adaptation of the parameters of the original harmony search algorithm. At present there are several types of algorithms that can solve complex real-world problems with uncertainty management. In this case the proposed method is in charge of optimizing the membership functions of three benchmark control problems (water tank, shower, and mobile robot). The main goal is to find the best parameters for the membership functions in the controller to follow a desired trajectory. Noise experiments are performed to test the efficacy of the method.

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Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization of Mathematical Functions

Cited by 44 publications

References 30 publications

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Modified Election Algorithm in Accelerating the Performance of Hopfield Neural Network for Random kSatisfiability

Donkey and smuggler optimization algorithm: A collaborative working approach to path finding

Optimization of Intelligent Controllers Using a Type-1 and Interval Type-2 Fuzzy Harmony Search Algorithm

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