Abstract
Real ants and bees are considered social insects, which present some remarkable characteristics that can be used, as inspiration, to solve complex optimization problems. This field of study is known as swarm intelligence. Therefore, this paper presents a new algorithm that can be understood as a simplified version of the well known Particle Swarm Optimization (PSO). The proposed algorithm allows saving some computational effort and obtains a considerable performance in the optimization of nonlinear functions. We employed four nonlinear benchmark functions, Sphere, Schwefel, Schaffer and Ackley functions, to test and validate the new proposal. Some simulated results were used in order to clarify the efficiency of the proposed algorithm.
Highlights
In natural systems, we commonly observe mechanisms where natural agents seem to be organized in a rational and ordered way
Despite the extremely low intellectual capacity of the individuals, the colony can solve surprisingly complex problems while searching for food (DORIGO et al, 1996; KARABOGA; AKAY, 2009; KARABOGA; BASTURK, 2007). Some of these natural agents are the main inspiration for interesting and powerful algorithms used in the search for optimal solutions of highly non-trivial problems (CHENG et al, 2009; KARABOGA, 2009; KARABOGA; BASTURK, 2007; TOKSARI, 2006; WANG et al, 2007) such as finding the global minimum of nonlinear functions, truss optimization, the classical traveling salesman problem, electric power systems, traffic flow, polymer design, the Schottky-Barrier estimation in
Several tests were carried out in order to compare the performance of the proposed algorithm with the well known ACO (Ant Colony Optimization) and ABC (Artificial Bee Colony) algorithms
Summary
We commonly observe mechanisms where natural agents seem to be organized in a rational and ordered way. Despite the extremely low intellectual capacity of the individuals, the colony can solve surprisingly complex problems while searching for food (DORIGO et al, 1996; KARABOGA; AKAY, 2009; KARABOGA; BASTURK, 2007) Some of these natural agents are the main inspiration for interesting and powerful algorithms used in the search for optimal solutions of highly non-trivial problems (CHENG et al, 2009; KARABOGA, 2009; KARABOGA; BASTURK, 2007; TOKSARI, 2006; WANG et al, 2007) such as finding the global minimum of nonlinear functions, truss optimization, the classical traveling salesman problem, electric power systems, traffic flow, polymer design, the Schottky-Barrier estimation in. Technology diode models and several other applications (ALRASHIDI; EL-HAWARY, 2009; HUANG et al, 2003; MARTINS et al, 2008; TEODOROVIC 2003; WANG; YE, 2009) This field of study is known as “swarm intelligence” and has attracted an increasingly number of researchers since the proposal of Particle Swarm Optimization (PSO) algorithm and of the Ant Colony Optimization (ACO) Algorithm (DORIGO et al, 1996)
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