Abstract
Nowadays, optimization techniques based on the analogy with swarming principles and collective activities of social species in nature have been used in the development of methodologies for solving a variety of real-world optimization problems. In this context, the social behavior of fish colonies has been recently explored to develop a novel algorithm, the so-called Fish Swarm Optimization Algorithm (FSOA), based on the behavior of fish swarm in search for food. In this paper, the FSOA is applied to four engineering systems, involving typical structural design and distillation column design. The results obtained are then compared with those obtained from other classical evolutionary approaches.
Highlights
Nowadays, the engineering system design using computational tools has become a major research field
An optimization technique known as Fish Swarm Optimization Algorithm (FSOA) based on the fish colonies was recently proposed by Li et al (2002)
2 FISH SWARM OPTIMIZATION ALGORITHM The FSOA is based on fish swarm observed in nature: approximately 50% of fish species live in swarm (i. e., present synchronous and coordinated movements) in some moment of their lives, as showed in Figure 1 (Li et al, 2002)
Summary
The engineering system design using computational tools has become a major research field. The development of optimization techniques based on analogies with swarming principles and collective activities of social species in nature (swarm intelligence) to solve real-world optimization problems characterizes an interesting and encouraging research topic. The most familiar representatives of swarm intelligence in optimization problems are the following: food-searching behavior of ants (Dorigo and Di Caro, 1999), particle swarm optimization (Shi and Eberhart, 2000), and artificial immune system (Castro and Timmis, 2002). In this context, an optimization technique known as Fish Swarm Optimization Algorithm (FSOA) based on the fish colonies was recently proposed by Li et al (2002). The conclusions and suggestions for future work conclude the paper
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