  The swarm intelligence of animals is a natural paradigm to apply to
optimization problems. Ant colony, bee colony, firefly and bat algorithms are
amongst those that have been demonstrated to efficiently to optimize complex
constraints. This paper proposes the new Sparkling Squid Algorithm (SSA) for
multimodal optimization, inspired by the intelligent swarm behavior of its
namesake. After an introduction, formulation and discussion of its
implementation, it will be compared to other popular metaheuristics. Finally,
applications to well - known problems such as image registration and the
traveling salesperson problem will be discussed.
