This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optim...

This paper presents an effective method for task scheduling in heterogeneous distributed systems. Its objective is to minimize the last task’s finish time and to maximize the system reliability probability. The optimum is carried out through a non-domination sort genetic algorithm. The experimental results based on both randomly generated graphs and the graphs of some real applications showed t...

• A hybrid machine learning strategy is developed for complex chemical processes. Continuum lumping kinetic embedded into the neural network framework. NSGA-II used multi-objective optimisation of hydrocracking process. Catalytic processes such as hydrocracking, gasification and pyrolysis play a vital role in renewable energy net zero transition. Due to non-linear behaviours during operation, c...