Interest in multimodal function optimization is expanding rapidly since realworld optimization problems often demand locating multiple optima within asearch space. This article presents a new multimodal optimization algorithmnamed as the Collective Animal Behavior (CAB). Animal groups, such as schoolsof fish, flocks of birds, swarms of locusts and herds of wildebeest, exhibit avariety of behaviors including swarming about a food source, milling around acentral location or migrating over large distances in aligned groups. Thesecollective behaviors are often advantageous to groups, allowing them toincrease their harvesting efficiency to follow better migration routes, toimprove their aerodynamic and to avoid predation. In the proposed algorithm,searcher agents are a group of animals which interact to each other based onthe biological laws of collective motion. Experimental results demonstrate thatthe proposed algorithm is capable of finding global and local optima ofbenchmark multimodal optimization problems with a higher efficiency incomparison to other methods reported in the literature.
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