A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

Abstract

The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO) algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front) maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The performance assessment is done by using the inverted generational distance (IGD) measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Assessment of Tribological performance of Coconut Shell Ash Particle Reinforced Al-Si-Fe Composites using Grey-Fuzzy Approach

“…Moreover, statistical approaches such as Taguchi method [15][16][17], Response Surface Methodology [10,18], Grey Relational Analysis (GRA) [14,[19][20][21]; soft computing techniques (ANN & ANFIS [22]) and artificial intelligence such as Genetic Algorithm (GA) [23], Particle Swarm Optimization (PSO) [24,25], and Teachinglearning-based optimization (TLBO) [26] techniques have been used to optimize the process parameters which influence tribological and machining behaviour of composites. However, it is observed that most of the results are not favourable due to the uncertainty associated with the process variables.…”

Integration of Simulated Quantum Annealing in Parallel Tempering and Population Annealing for Heterogeneous-Profile QUBO Exploration

“…In recent years, many other cooperative population-based approaches have been proposed, for example, teachinglearning optimization [12], [13], which mimics the learning process of a class. In particular, there is a class of students, whose scores are the quality of the associated solution.…”

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

“…Majority of the novel single-objective algorithms have been furnished with appropriate mechanisms to deal with multi-objective problems (MOP) also. Few of them are Non-sorting Genetic Algorithm (Deb et al, 2000), Strength Pareto Evolutionary Algorithm (SPEA-II) (Zitzler et al, 2001), Multi-objective Particle Swarm Optimization (MOPSO) (Coello & Lechuga, 2002), Dragonfly Algorithm (Mirjalili, 2016), Multi-objective Jaya Algorithm (Rao et al, 2017), Multi-objective improved Teaching-Learning based Algorithm (MO-iTLBO) (Rao & Patel, 2014), Multi-objective Bat Algorithm (MOBA) (Yang, 2011), Multi-objective Ant Lion Optimizer (MOALO) , Multi-objective Bee Algorithm (Akbari et al, 2012), Non-dominated sorting MFO (NSMFO) , Multi-objective Grey Wolf Optimizer (MOGWO) (Mirjalili et al, 2016), Multi-objective Sine Cosine Algorithm (MOSCA) , Multi-objective water evaporation algorithm (MOWCA) (Sadollah & Kim, 2015) and so forth.…”