Weight based fish school search

This work further investigates how weight based FSS (i.e. almost only use of local information) can impact in the automatic splitting the school for solution of multimodal bench mark problems. The chief modification to standard FSS, in order to produce the wFSS, was the introduction of a relationship among fish solely relying on factual already existing indications of individual success. The implementation resulted in a lighter algorithm (when compared to other FSS attempts to solve multimodal problems) and a method that produces more suitable solution candidates for optimization problems. Following a complexity analysis, that reveal a reduction from O(n⁴) of dFSS to O(n²) of wFSS, a thorough performance comparison with two other competing techniques was carried out using four different metrics, highly appropriate to assess multiobjective optimization. The experiments showed the upper hand of wFSS in multimodal continuous optimization adherent to the design decisions of (i) no use of global information for splitting the swarm, (ii) no heavy increase in computational costs to FSS and (iii) abidance to the original principle of FSS of not using topological information to solve the optimization problem.