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Happiness Optimizer: a swarm intelligence algorithm for finding global minimum ArefYelghi
  • میراث آلبرتا
میراث آلبرتا

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Recent work attempted to demonstrate the global best minimum in complex problems. This paper proposes a population and direct-based swarm optimization algorithm called as HPO algorithm. The HPO algorithm is designed by inspired of personal behaviorand demonstrated in the 30 and 100 dimensionson benchmark functions. The model have four concepts: what you want?, what you have?, what others have?, what is happened?. These concepts take into account the balancing between exploration and exploitation operator and demonstrate its efficiency, robustness and stability insynthetic and real problems.In experiment, we consider 15 benchmark functions include unimodal and multimodal characteristic of functions.For compression, our algorithm and some well-known algorithms with 30 times run and applied on the benchmark functions and compared with statistical value and Wilcoxon signed-rank test. As a consequence, the performance and convenient ofour work aredemonstratedbetter than the others. 1-INTRUDOCTION Many real-world applications include the complexity problem that should be optimize and then applied on the real work. The purpose of optimization is the minimization or maximization of fitness function for real problem such as energy consumption, designing, routing, transportation et al. The performance, efficiency and sustainability of optimization algorithms are important in solving complex problem. In many case, optimization problem is highly complex and nonlinear function, whom scientist attempts to solve the problem by using popular methods of soft computing. In recent years, metaheuristic algorithms have been used and applied on the real problems in engineering field [1-5]. The advantage of metaheuristic algorithm rather than the deterministic algorithm is scape of trap from local minimum state. There are two popular methodology such as Evolutionary Algorithms (EA) and Swarm Intelligence (SI), which have new landscape for the complex problem in the metaheuristics optimization algorithm. The advantages of them are the power of based-population solution and providea new solutionby considering the balance of exploration and exploitation.Thepoint of view of efficiency and balancing of them should be se the best solution by escaping from many local minimum.By inspired of Darwin's theory the Evolutionary Optimization techniques are presented and used in real problems. The principle of mechanism includes selection, mutation and crossover operation. Genetic algorithm is one of EA which is popular in metaheuristic algorithm (GA) and have rigorous mathematical analyses [6-7].And some works with based on this paradigm is tabu search [9], simulated annealing [10], forest optimization algorithm [11], biogeography-based optimizer (BBO) [12], Evolutionary Programing (EP) [13], Evolution Strategy (ES) [14].Beni and Wang in 1993 presented concept of swarm intelligence, which include simulation of behavior of living creature [15]. Scientist attempt to find local rule between creatures and then convert to aalgorithm for using in soft computing.The other algorithmsare computational method, which based on directed best agent. The framework of them are repeatedlytrying to improve a nomineesolution in relation toa given measure of quality fitness.Examples of SI-based approaches are particle swarm optimization [16], Glowworm algorithm [17] Intelligent water drops [18], Cat