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This work presents a novel approach to single objective optimization using the amoeba Dictyostelium discoideum (Dd), sometimes known as Slime Mold, as its basis. A short explanation of the biological background of Dd is presented. Inspirations taken from existing computational biology and educational simulation studies of Dd also are provided. Based upon previous works, an algorithm for optimization is constructed called the Slime Mold Optimization Algorithm.

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Dynamic analyses of canonical particle swarm optimization (PSO) have indicated that parameter values of ϕ_max = 4.1 and constriction coefficient χ = 0.729 provide adequate exploration and prevent swarm explosion. This paper shows by example that these values do not prevent swarm explosion in some cases. In other examples it is shown that even when the swarm does not explode, the canonical PSO algorithm with these parameter values can still fail to converge indefinitely. A satisfactory analysis of PSO has yet to be made, and will require abandoning certain assumptions that oversimplify particle behavior.