A new non-dominated sorting ions motion algorithm: Development and applications

Abstract

This paper aims a novel and a useful multi-objective optimization approach named Non-Dominated Sorting Ions Motion Algorithm (NSIMO) built on the search procedure of Ions Motion Algorithm (IMO). NSIMO uses selective crowding distance and non-dominated sorting method to obtain various non-domination levels and preserve diversity amongst the best set of solutions. The suggested technique is employed to various multi-objective benchmark functions having different characteristics like convex, concave, multimodal, and discontinuous Pareto fronts. The recommended method is analyzed on different engineering problems having distinct features. The results of the proposed approach are compared with other well-regarded and novel algorithms. Furthermore, we present that the projected method is easy to implement, capable of producing a nearly true Pareto front and algorithmically inexpensive. .

Multi-objective Geometric Mean Optimizer (MOGMO): A Novel Metaphor-Free Population-Based Math-Inspired Multi-objective Algorithm

“…With numerous optimal solutions in the MOO realm, many algorithms use an archive (or repository) to store superior solutions, refining this archive throughout the optimization process. Recent years have seen the development of various novel and efficient Pareto-based MOEAs, each with unique mechanics, such as the multi-objective ant lion optimizer [22], MO equilibrium optimizer (MOEO) [23], MO slime mould algorithm [24], MO arithmetic optimization algorithm [25], non-dominated sorting ions motion algorithm [26], social cognitive optimization algorithm [27], multi-objective multi-verse optimization (MOMVO) [28], non-dominated sorting grey wolf optimizer [29], MO Gradient-Based Optimizer [30], MO plasma generation optimizer (MOPGO) [31], non-dominated sorting Harris hawks optimization [32], MO thermal exchange optimization [33], decomposition based multi-objective heat transfer search [34], Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D) [35], MOGNDO Algorithm [36], Non-dominated sorting moth flame optimizer [37], Non-dominated sorting whale optimization algorithm [38] and Non-Dominated Sorting Dragonfly Algorithm [39]. However, the No-Free-Lunch theorem (NFL) [40] highlights that no single optimization technique can universally solve all MOPs, underscoring the need for continuous refinement of existing algorithms or the development of new ones.…”

Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm

“…Assume the irradiance of the solar irradiance performance β PDF and CDF are implemented to represent it according to (5) and (6) [27].…”

“…Multi-objective optimization is much more convoluted than single-objective optimization because of the presence of multiple optimal solutions. At large, all solutions are conflicting and hence a group of non-dominated solutions is required to be found out to approximate the true Pareto front [5]. Most of the novel single-objective algorithms have been assorted with convenient mechanisms to transact with multi-objective problems (MOP) also such as Mirjalili et al offered Ant Lion Optimizer (MOALO) method in 2017 [6].…”

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

“…Other popular multi-objective (MO) Algorithms include MO ant lion optimizer (MOALO) 43 , MO equilibrium optimizer (MOEO) 44 , MO slime mould algorithm (MOSMA) 45 , MO arithmetic optimization algorithm (MOAOA) 46 , non-dominated sorting ions motion algorithm (NSIMO) 47 , MO marine predator algorithm (MOMPA) 48 , multi-objective multi-verse optimization (MOMVO) 49 , non-dominated sorting grey wolf optimizer (NS-GWO) 50 , MO gradient-based optimizer (MOGBO) 51 , MO plasma generation optimizer (MOPGO) 52 , non-dominated sorting Harris hawks optimization (NSHHO) 53 , MO thermal exchange optimization (MOTEO) 54 , decomposition based multi-objective heat transfer search (MOHTS/D) 55 , Decomposition-Based Multi-Objective Symbiotic Organism Search (MOSOS/D) 56 , MOGNDO Algorithm 57 , Non-dominated sorting moth flame optimizer (NSMFO) 58 , Non-dominated sorting whale optimization algorithm (NSWOA) 59 , Non-Dominated Sorting Dragonfly Algorithm (NSDA) 60 , a reference vector based multiobjective evolutionary algorithm with Q-learning for operator adaptation 61 , a many-objective evolutionary algorithm based on hybrid dynamic decomposition 62 and use of two penalty values in multiobjective evolutionary algorithm based on decomposition 63 .…”