Multi-Objective Ant Lion Optimizer for Solving Power System Economic Dispatch Problems

The Multi-Objective Ant Lion Optimizer (MOALO) is a nature-inspired intelligent optimization algorithm that mimics the predatory behavior of antlions in nature. Recently, it has been successfully applied to complex multi-objective problems like power system economic dispatch. The algorithm achieves efficient search in the solution space by simulating the mechanism of antlions constructing funnel-shaped traps and preying on ants.

In power system economic dispatch problems, MOALO simultaneously optimizes multiple conflicting objectives such as minimizing fuel costs, reducing pollutant emissions, and maintaining grid stability. The algorithm maintains an external archive to store non-dominated solutions and uses strategies like crowding distance to preserve the diversity of the Pareto front. Compared to its single-objective version, MOALO improves the elite selection mechanism and population update rules, enabling the algorithm to balance convergence and distribution properties.

A typical implementation involves three main phases: First, random initialization of antlion and ant population positions using uniform distribution functions. Second, through an adaptive selection mechanism that allows superior antlions (representing better solutions) more hunting opportunities. Third, employing a dynamic boundary contraction strategy that mimics antlions tightening their traps, gradually refining the search space. Code implementation typically includes fitness evaluation functions, dominance comparison routines, and archive management modules. Testing shows that when solving dispatch problems with nonlinear constraints like valve-point effects and multi-fuel characteristics, MOALO demonstrates better convergence speed and solution set distribution uniformity compared to traditional multi-objective algorithms like NSGA-II.

Practical applications require attention to: Objective function weight design based on actual dispatch requirements, where algorithm parameters such as population size and iteration count significantly impact computational efficiency. Constraint handling mechanisms (like penalty function methods) are crucial for obtaining feasible solutions. Future developments could integrate deep learning methods to further enhance algorithm performance in high-dimensional problems through neural network-assisted search strategies.