Interpretable Self-Organizing Maps (iSOM) for Visualization of Pareto Front in Multiple Objective Optimization

Nagar, Deepak; Ramu, Palaniappan; Deb, Kalyanmoy

doi:10.1007/978-3-030-72062-9_51

Cited by 3 publications

(1 citation statement)

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“…Moreover, visualization of the multidimensional results in the decision space can be important to gain insight into the Pareto optimal solutions. For example, the multidimensional scaling (MDS) and self-organizing maps (SOM) methods have proved their worth in solving multi-objective optimization (MOO) problems [28], and the recent application can be found in [29].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm

Khunkitti

Siritaratiwat

Premrudeepreechacharn

2021

Sustainability

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Solving the optimal power flow problems (OPF) is an important step in optimally dispatching the generation with the considered objective functions. A single-objective function is inadequate for modern power systems, required high-performance generation, so the problem becomes multi-objective optimal power flow (MOOPF). Although the MOOPF problem has been widely solved by many algorithms, new solutions are still required to obtain better performance of generation. Slime mould algorithm (SMA) is a recently proposed metaheuristic algorithm that has been applied to solve several optimization problems in different fields, except the MOOPF problem, while it outperforms various algorithms. Thus, this paper proposes solving MOOPF problems based on SMA considering cost, emission, and transmission line loss as part of the objective functions in a power system. The IEEE 30-, 57-, and 118-bus systems are used to investigate the performance of the SMA on solving MOOPF problems. The objective values generated by SMA are compared with those of other algorithms in the literature. The simulation results show that SMA provides better solutions than many other algorithms in the literature, and the Pareto fronts presenting multi-objective solutions can be efficiently obtained.

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Section: Introductionmentioning

confidence: 99%