An Adaptive Reference Vector-Guided Evolutionary Algorithm Using Growing Neural Gas for Many-Objective Optimization of Irregular Problems

A dual-population Constrained Many-Objective Evolutionary Algorithm based on reference point and angle easing strategy

Constrained many-objective optimization problems (CMaOPs) have gradually emerged in various areas and are significant for this field. These problems often involve intricate Pareto frontiers (PFs) that are both refined and uneven, thereby making their resolution difficult and challenging. Traditional algorithms tend to over prioritize convergence, leading to premature convergence of the decision variables, which greatly reduces the possibility of finding the constrained Pareto frontiers (CPFs). This results in poor overall performance. To tackle this challenge, our solution involves a novel dual-population constrained many-objective evolutionary algorithm based on reference point and angle easing strategy (dCMaOEA-RAE). It relies on a relaxed selection strategy utilizing reference points and angles to facilitate cooperation between dual populations by retaining solutions that may currently perform poorly but contribute positively to the overall optimization process. We are able to guide the population to move to the optimal feasible solution region in a timely manner in order to obtain a series of superior solutions can be obtained. Our proposed algorithm's competitiveness across all three evaluation indicators was demonstrated through experimental results conducted on 77 test problems. Comparisons with ten other cutting-edge algorithms further validated its efficacy.

Many-objective ant lion optimizer (MaOALO): A new many-objective optimizer with its engineering applications

Many-objective optimization (MaO) is an important aspect of engineering scenarios. In many-objective optimization algorithms (MaOAs), a key challenge is to strike a balance between diversity and convergence. MaOAs employs various tactics to either enhance selection pressure for better convergence and/or implements additional measures for sustaining diversity. With increase in number of objectives, the process becomes more complex, mainly due to challenges in achieving convergence during population selection. This paper introduces a novel Many-Objective Ant Lion Optimizer (MaOALO), featuring the widely-popular ant lion optimizer algorithm. This method utilizes reference point, niche preserve and information feedback mechanism (IFM), to enhance the convergence and diversity of the population. Extensive experimental tests on five real-world (RWMaOP1- RWMaOP5) optimization problems and standard problem classes, including MaF1-MaF15 (for 5, 9 and 15 objectives), DTLZ1-DTLZ7 (for 8 objectives) has been carried out. It is shown that MaOALO is superior compared to ARMOEA, NSGA-III, MaOTLBO, RVEA, MaOABC-TA, DSAE, RL-RVEA and MaOEA-IH algorithms in terms of GD, IGD, SP, SD, HV and RT metrics. The MaOALO source code is available at: https://github.com/kanak02/MaOALO.

Use of Two Penalty Values in Multiobjective Evolutionary Algorithm Based on Decomposition

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) with the penalty-based boundary intersection (PBI) function (denoted as MOEA/D-PBI) has been frequently used in many studies in the literature. One essential issue in MOEA/D-PBI is its penalty parameter value specification. However, it is not easy to specify the penalty parameter value appropriately. This is because MOEA/D-PBI shows different search behavior when the penalty parameter values are different. The PBI function with a small penalty parameter value is good for convergence. However, the PBI function with a large value of penalty parameter is needed to preserve the diversity and uniformity of solutions. Although some methods for adapting the penalty parameter value for each weight vector have been proposed, they usually lead to slow convergence. In this article, we propose the idea of using two different values of penalty parameter simultaneously in MOEA/D-PBI. Although the idea is simple, the proposed algorithm is able to utilize both the convergence ability of a small penalty parameter value and the diversification ability of a large penalty parameter value of the PBI function. Experimental results demonstrate that the proposed algorithm works well on a wide range of test problems.