Development and Analysis of Metaheuristic Based Technique for Solving Multi Objective Problems of Optimization

Abstract

Optimization involves selecting the most favorable choice from a given set of options, aiming newlineto maximize or minimize a specific function within its defined domain. This process crucial newlinefor tackling real-world problems, relies on various mathematical techniques known as newlinequotOptimization Techniquesquot to determine the optimal value of a function. However, real-world newlineoptimization challenges often exhibit complex characteristics such as multimodality, newlinediscontinuity, non-linearity, non-differentiability, and non-convexity, making traditional newlinegradient-based methods impractical. Metaheuristic optimization algorithms have grown in newlinepopularity as a solution to these difficulties. Metaheuristics, unlike gradient-based techniques, newlinedo not rely on gradient knowledge and instead use stochastic mechanisms to accurately newlineestimate near-optimal solutions. These algorithms are frequently used in scientific and newlineindustrial areas because of their simplicity, capacity to treat complex optimization problems as newlineblack boxes, and ability to avoid local optima. newlineThe majority of metaheuristic algorithms are inspired by nature and uses swarm intelligence newlineprinciples. Individual agents in swarm intelligence may not be very intelligent but they newlinedemonstrate emergent self-organization, allowing for successful exploration of enormous newlinesolution spaces. Nature-inspired metaheuristics use this idea to efficiently navigate complex newlineoptimization landscapes frequently incorporating memory mechanisms to store promising newlinesolutions and selecting strategies for solution refining. newlineThe Ant Lion Optimizer (ALO), Genetic Algorithm (GA), and Particle Swarm Optimization newline(PSO) are three well-known metaheuristic algorithms. Despite their extensive use and newlineefficiency, there is still potential for development in every aspect of metaheuristics. The major newlinedifficulty is to strike the appropriate balance between exploration and exploitation during newlinevarious search phases. Since its conception, the Ant Lion Optimizer (ALO) has undergone newlinesignificant improvements. Researchers have relea