Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

Mohamed, Ali Wagdy; Hadi, Anas A.; Mohamed, Ali Khater

doi:10.1007/s13042-019-01053-x

Cited by 344 publications

(205 citation statements)

References 94 publications

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“…FIGURE 1: Types of Nature-Inspired Metaheuristic Algorithms [9] Metaheuristic algorithms are mostly inspired by natural phenomena. They can be classified based on their sources [9], as depicted in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

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Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

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Section: Introductionmentioning

confidence: 99%

“…Gaining-sharing knowledge based algorithm [9] 2019 optimization algorithms are presented in Table 3. 4) Swarm based Algorithms -Swarm based metaheuristics are inspired by the social behavior of insects or animals.…”

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confidence: 99%

Border Collie Optimization

Dutta

Bhattacharyya

Dey³

et al. 2020

IEEE Access

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“…A large number of practices have demonstrated that bionic algorithms, such as Genetic Algorithm [1], Particle Swarm Optimization Algorithm [2], Differential Evolution Algorithm [3], Ant Colony Algorithm [4], Fruit Fly Algorithm [5], Artificial Fish Swarm Algorithm [6], Artificial Bee Colony Algorithm [7], Chicken Swarm Optimization Algorithm [8], Monkey Algorithm [9], Bat Algorithm [10], Cuckoo Search Algorithm [11], Whale Optimization Algorithm [12], etc., show prospective and excellent capability in solving various kinds of optimization issues. In recently years, there are also many other new Optimization Algorithms have been proposed, such as adaptive guided differential evolution Algorithm [41], differential Evolution Algorithm [42], Naked Mole Rat Algorithm [43] and Improved Versions of Whale Optimization Algorithm [44], Spherical search algorithm [45] and Gaining-sharing knowledge based algorithm [46], real-parameter optimization JSO algorithm [47], LSHADE algorithm with semi-parameter adaptation hybrid with CMA-ES [48], butterfly optimizer (BO) optimization algorithm [49], EBLSHADE Algorithms for Global Numerical Optimization [50], Real-Parameter Unconstrained Optimization Based on Enhanced AGDE Algorithm [51].…”

Section: Introductionmentioning

confidence: 99%

Research on New Adaptive Whale Algorithm

Chen

2020

IEEE Access

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Bionic algorithms have always played an important role in industrial, agricultural, and scientific research. The optimization of bionics algorithms has always been the focus of scholars in various countries. A whale algorithm based on optimization based on adaptive convergence and Levy features (IWOA) is proposed to overcome the disadvantages, such as low precision, slow convergence speed and tending to involve the local optimum of the whale algorithm. The improved Bernouilli Shift map is used to initialize the population to maintain diversity of the population. Optimizing the adaptive convergence factor is able to balance the local and global optimization ability. The Levy flight mechanism is introduced to optimize foraging behavior and improve global searching ability. In addition, the trigger rule is applied to screen individuals after each iteration to maintain individual vitality and enhance overall performance of the algorithm. In the simulation, IWOA, Ant Colony Optimization, Particle Swarm Optimization, Whale Optimization Algorithm and the optimized whale algorithms CWOA, LWOA are compared using the 20 classical test functions. The simulation results demonstrate that the IWOA algorithm possesses good global and local searching ability, especially in solving multi-peak and high-dimensional functions.

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“…Recently, a great interest has been given to using some artificial tools in optimization. Metaheuristics play a great role in either real-life simulation or invoking intelligent learned procedures [15][16][17][18][19][20][21][22][23][24]. Metaheuristics exhibit good degrees of robustness for a wide range of problems.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based EDAs for Stochastic Programming Problems

2020

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With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments.

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Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

Cited by 344 publications

References 94 publications

Border Collie Optimization

Border Collie Optimization

Research on New Adaptive Whale Algorithm

Simulation-Based EDAs for Stochastic Programming Problems

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