Chicken Swarm Optimization Based on Elite Opposition-Based Learning

Abstract

Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

Search Result Enhancement For Arabic Datasets Using Modified Chicken Swarm

“…Where is term frequency and is the inverse document frequency and common it formulated a log ( ⁄ ), where N is the size of the document collection and is the document frequency [12]. The factor is normalized by the maximum in the query vector [11].…”

“…As for the hens, they can follow up their group-mate roosters to seeking for food and randomly theft the food found by other the individuals. Equation-6 used to update hens position [12].…”

“…... (9) ) … . (10) Where X m;j (t) is referring to the chick's mother position, F [0,2] is the flow coefficient, which marks that the chick follows its mother to forage food [12].…”

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

“…The rest of the elements of the generated binary solution are created in such a way that their values depend on corresponding elements on the th row of CBE. From the computation steps (5)- (11), the value of determines the probability of to be the same as . The higher value of means the higher correlation between elements and and consequently the higher probability that will be set equal to .…”

“…One of them is a metaheuristic (MH). The term metaheuristics can cover nature-inspired optimisers [1][2][3][4][5][6][7][8][9][10], swarm intelligent algorithms [11][12][13][14][15][16][17][18][19][20], and evolutionary algorithms [21][22][23][24]. Most of them are based on using a set of design solutions, often called a population, for searching an optimum.…”