Patent ID: 8793200

Claim:
A system implementing particle swami optimization, the system comprising one or more processors that are configured to perform operations of: operating a plurality of software agents as a cooperative swarm to locate an optimum of an objective function, wherein each agent is assigned an initial velocity vector to explore a multi-dimensional solution space, where each agent is configured to perform at least one iteration, the iteration being a search in the multi-dimensional solution space for the optimum of the objective function, where each agent keeps track of a first position vector representing a current best solution y i that the agent has identified, and a second position vector used to store the current global best solution y g among all agents; wherein in a first phase, the plurality of software agents randomly explore the multi-dimensional solution space utilizing a random walk process to locate, the optimum of the objective function; wherein in a second phase that follows the first phase, the velocity and position vectors for a particle i are updated probabilistically to locate the optimum of the objective function; wherein in the first phase, each agent is driven by a random force according to the following: v -> i ⁡ ( t + 1 ) = w ⁢ v -> i ⁡ ( t ) + c 0 ⁢ q -> 0 ⁡ ( t ) x -> i ⁡ ( t + 1 ) = x -> i ⁡ ( t ) + χ ⁢ v -> i ⁡ ( t + 1 ) , wherein for t≧1, y i and y g and are computed according to: y -> i ⁡ ( t + 1 ) = { x -> i ⁡ ( t + 1 ) , if ⁢ ⁢ J ⁡ ( x -> i ⁡ ( t + 1 ) ) > J ⁡ ( y -> i ⁡ ( t ) ) y -> i ⁡ ( t ) , otherwise ⁢ ⁢ y -> g ⁡ ( t + 1 ) = arg ⁢ ⁢ max y -> i ⁢ J ⁡ ( y -> i ⁡ ( t + 1 ) ) , where {right arrow over (x i )}(t) is a position vector and {right arrow over (v i )}(t) is a velocity vector at an iteration t of an i-th agent, w is a momentum constant that prevents premature convergence of the agents, x is a constriction factor which influences the convergence of the agents, c 0 is a constant, q 0 (t) is a vector with the same dimension as {right arrow over (v i )} or {right arrow over (x i )} with a set of uniformly distributed random components in [−1.0, 1.0] drawn on each iteration, wherein the current best y i and the global best y g computed in the first phase are used as an initial current best y i and global best y g in the second phase; wherein the first phase runs for a predetermined number of iterations prior to initiation of the second phase; and wherein the plurality of software agents converge at a position in the multi-dimensional solution space representing an optimum of the objective function, wherein the objective function is J({right arrow over (y)} g ).