abstract
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.