# Multiobjective-SMA
This paper proposes a multi-objective Slime Mould Algorithm (MOSMA), a multi-objective variant of the recently-developed Slime Mould Algorithm (SMA) for
handling the multi-objective optimization problems in industries. Recently, for handling optimization problems, several meta-heuristic and evolutionary
optimization techniques have been suggested for the optimization community. These methods tend to suffer from low-quality solutions when evaluating
multi-objective optimization (MOO) problems than addressing the objective functions of identifying Pareto optimal solutions’ accurate estimation and increasing
the distribution throughout all objectives. The SMA method follows the logic gained from the oscillation behaviors of slime mould in the laboratory experiments.
The SMA algorithm shows a powerful performance compared to other well-established methods, and it is designed by incorporating the optimal food path using the
positive-negative feedback system. The proposed MOSMA algorithm employs the same underlying SMA mechanisms for convergence combined with an elitist non-dominated
sorting approach to estimate Pareto optimal solutions. As a posteriori method, the multi-objective formulation is maintained in the MOSMA, and a crowding distance
operator is utilized to ensure increasing the coverage of optimal solutions across all objectives. To verify and validate the performance of MOSMA, 41 different
case studies, including unconstrained, constrained, and real-world engineering design problems are considered. The performance of the MOSMA is compared with
Multiobjective Symbiotic-Organism Search (MOSOS), Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Multiobjective Water-Cycle Algorithm
(MOWCA) in terms of different performance metrics, such as Generational Distance (GD), Inverted Generational Distance (IGD), Maximum Spread (MS), Spacing, and
Run-time. The simulation results demonstrated the superiority of the proposed algorithm in realizing high-quality solutions to all multi-objective problems,
including linear, nonlinear, continuous, and discrete Pareto optimal front. The results indicate the effectiveness of the proposed algorithm in solving
complicated multi-objective problems.
【智能优化算法-粘菌算法】基于粘菌算法MOSMA求解多目标优化问题附matlab代码.zip
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