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pipisusu's Projects

aircrewschedulingproblems

Solving Air Crew Scheduling Problems using Genetic Algorithms

benchmarkfcns

A collection of mathematical test functions for benchmarking optimization algorithms.

cec2019

100-Digit Competition. This folder includes GECCO 2019 and SEMCCO 2019 too, in addition to CEC 2019

chaotic-gsa-for-engineering-design-problems

All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers are now using chaotic maps. The Chaotic Gravitational Search Algorithm (CGSA) is a physics-based heuristic algorithm inspired by Newton's gravity principle and laws of motion. It uses 10 chaotic maps for global search and fast convergence speed. Basically, in GSA gravitational constant (G) is utilized for adaptive learning of the agents. For increasing the learning speed of the agents, chaotic maps are added to gravitational constant. The practical applicability of CGSA has been accessed through by applying it to nine Mechanical and Civil engineering design problems which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), Speed Reducer Design (SRD), Gear Train Design (GTD), Three Bar Truss (TBT), Stepped Cantilever Beam design (SCBD), Multiple Disc Clutch Brake Design (MDCBD), and Hydrodynamic Thrust Bearing Design (HTBD). The CGSA has been compared with seven state of the art stochastic algorithms particularly Constriction Coefficient based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Standard Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CGSA shows efficient performance as compared to other seven participating algorithms.

codes

Codes for some of my co-authored journal/conference papers

comparative-study-of-truss-sizing-optimisation-problems-in-matlab-2022

Matlab codes: Comparative study of truss sizing optimisation problem 2022 (add new algorithms)

covid-19-seir

a modified version of SEIR model to predict equilibrium points for COVID-19 pandemic

electricity-gas-ann

Data and codes for the ANN-based electricity-gas dispatch article on Applied Energy (https://www.sciencedirect.com/science/article/pii/S030626192100043X).

fonm

Fractional Order Numerical Methods

hsmea

Hybrid Surrogate Assisted Many-objective Evolutionary Algorithm for Expensive Multi/Many Objective Optimization Problems

madde

marine-predators-algorithm

meta-heuristic-algorithms

This repository is used to implement and analyze nature inspired computing algorithms on various benchmark function. We also try to solve some real world problems by MHA.