3,0.1)foriinrange(num):ori_w_p_x=random.choice(ori_w_range_x)ori_w_p_y=random.choice(ori_w_range_y)ori_w_p=[ori_w_p_x,ori_w_p_x]#wolves_dict['wolf_'+str(i)]=[ori_w_p[0],ori_w_p[1],get_fitness(ori_w_p)]wolves_dic
Code importnumpyasnpimportrandomimportcopydefget_fitness(ori_w_p):# 设置适应度函数# return np.absolute(np.sin(ori_w_p[0])*np.cos(ori_w_p[1]) + np.sin((ori_w_p[0]-10)/10)*2 + np.sin((ori_w_p[1]-10)/10))*2returnnp.sin(ori_w_p[0]**2)+np.cos(ori_w_p[1]**2...
Considering the source size and location, line loading limits, generator capacity, and bus parameters, the Grey Wolf optimization algorithm determines the optimum solution for the capacity expansion. Thus, an effective method is proposed for power system planning. According to the optimization results ...
pythonalgorithmoptimizationgenetic-algorithmevolutionary-algorithmsant-colony-optimizationdifferential-evolutioncuckoo-searchoptimization-algorithmsparticle-swarm-optimizationnature-inspired-computationartificial-bee-colonysimulated-annealing-algorithmnature-inspired-algorithmshuristicniabioinspiredgrey-wolf-optimizerevolutionary-prog...
and resource usage cost. The formulated problem is proven to be an NP-hard one. Thus, we develop an evolutionary meta-heuristic solution for the offloading problem, namely WOLVERINE, based on a Binary Multi-objective Grey Wolf Optimization algorithm that achieves a feasible solution within polynomia...
algorithm is based on the actual nonlinear model, which takes the minimum average value of reprojection error as the objective function. The grey wolf position is randomly generated within a given range. Then, the grey wolf optimization algorithm based on levy flight and mutation mechanism is used...
A python library for: Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Cuckoo Search,
The GWO algorithm works by simulating the social hierarchy and hunting behavior of grey wolves. The algorithm starts with an initial population of grey wolves, where each wolf represents a solution to the optimization problem. The algorithm then updates the positions of the wolves based on their ...
Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization proble
This theoretical adjustment enhances the exploration capabilities of the algorithm, making it less likely to get trapped in local optima early in the process. Improved Exploration and Exploitation The Enhanced Grey Wolf Optimization (EGWO) algorithm modified the omega wolf position update which ...