Stability Analysis of Artificial Bee Colony Optimization Algorithm

Theoretical analysis of swarm intelligence and evolutionary algorithms is relatively less explored area of research. Stability and convergence analysis of swarm intelligence and evolutionary algorithms can help the researchers to fine tune the parameter values. This paper presents the stability analysis of a famous Artificial Bee Colony (ABC) optimization algorithm using von Neumann stability criterion for two-level finite difference scheme. Parameter selection for the ABC algorithm is recommended based on the obtained stability conditions. The findings are also validated through numerical experiments on test problems

Analysing convergence, consistency and trajectory of Artificial Bee Colony Algorithm

Recently, swarm intelligence based algorithms gained attention of the researchers due to their wide applicability and ease of implementation. However, much research has been made on the development of swarm intelligence algorithms but theoretical analysis of these algorithms is still a less explored area of the research. Theoretical analyses of trajectory and convergence of potential solutions towards the equilibrium point in the search space can help the researchers to understand the iteration-wise behaviour of the algorithms which can further help in making them efficient. Artificial Bee Colony (ABC) optimization algorithm is swarm intelligence based algorithm. This paper presents the convergence analysis of ABC algorithm by using results from the theory of dynamical system and convergent boundaries for the parameters $\phi$ and $\psi$ is proposed. Also the trajectory of potential solutions in the search space is analysed by obtaining a partial differential equation corresponding to the position update equation of ABC algorithm. The analysis reveals that the ABC algorithm performs better/efficiently when parameters $\phi$ and $\psi$ are in the convergent region and potential solutions movement follows 1-Dimensional advection equation

Design of wind farm layout with non-uniform turbines using fitness difference based BBO

Biogeography-based optimization (BBO) is an emerging meta-heuristic algorithm. BBO is inspired from the migration of species from one island to another. This study presents the solution of the wind farm layout optimization problem with wind turbines having non-uniform hub heights and rotor radii using BBO and an improved version of BBO. This study proposes an improved version of BBO, Fitness Difference Based BBO (FD-BBO). FD-BBO is obtained by incorporating the concept of fitness differences in original BBO. First, in order to justify the superiority of FD-BBO over BBO, it is tested over $15$ standard test problems of optimization. The numerical results of FD-BBO are compared with the original version of BBO and an advanced version of BBO, Blended BBO (BBBO). Through graphical and statistical analyses, FD-BBO is established to be an efficient and accurate algorithm. The BBO, BBBO and FD-BBO are than applied to solve the wind farm layout optimization problem. In the considered problem, not only the location of the wind turbines but hub heights and rotor radii are also taken as decision variables. Two cases of the problems are dealt: $26$ turbines in the farm size of $2000m$ $\times$ $2000m$ and $30$ turbines in the farm size of $2000m$ $\times$ $2000m$. Numerical results are compared with earlier published results and that of original BBO and Blended BBO. It is found that FD-BBO is the better approach to solving the problem under consideration

Sine Cosine Algorithm for Optimization

This open access book serves as a compact source of information on sine cosine algorithm (SCA) and a foundation for developing and advancing SCA and its applications. SCA is an easy, user-friendly, and strong candidate in the field of metaheuristics algorithms. Despite being a relatively new metaheuristic algorithm, it has achieved widespread acceptance among researchers due to its easy implementation and robust optimization capabilities. Its effectiveness and advantages have been demonstrated in various applications ranging from machine learning, engineering design, and wireless sensor network to environmental modeling. The book provides a comprehensive account of the SCA, including details of the underlying ideas, the modified versions, various applications, and a working MATLAB code for the basic SCA

Fitness Varying Gravitational Constant in GSA

Gravitational Search Algorithm (GSA) is a recent metaheuristic algorithm inspired by Newton's law of gravity and law of motion. In this search process, position change is based on the calculation of step size which depends upon a constant namely, Gravitational Constant (G). G is an exponentially decreasing function throughout the search process. Further, inspite of having different masses, the value of G remains same for each agent, which may cause inappropriate step size of agents for the next move, and thus leads the swarm towards stagnation or sometimes skipping the true optima. To overcome stagnation, we first propose a gravitational constant having different scaling characteristics for different phase of the search process. Secondly, a dynamic behavior is introduced in this proposed gravitational constant which varies according to the fitness of the agents. Due to this behavior, the gravitational constant will be different for every agent based on its fitness and thus will help in controlling the acceleration and step sizes of the agents which further improve exploration and exploitation of the solution search space. The proposed strategy is tested over 23 well-known classical benchmark functions and 11 shifted and biased benchmark functions. Various statistical analyses and a comparative study with original GSA, Chaos-based GSA (CGSA), Bio-geography Based Optimization (BBO) and DBBO has been carried out

Computationally Intelligent Systems and their Applications

This book covers all core technologies like neural networks, fuzzy systems, and evolutionary computation and their applications in the systems. Computationally intelligent system is a new concept for advanced information processing. The objective of this system is to realize a new approach for analyzing and creating flexible information processing of sensing, learning, recognizing, and action taking. Computational intelligent is a part of artificial intelligence (AI) which includes the study of versatile components to empower or encourage savvy practices in intricate and evolving situations. The computationally intelligent system highly relies on numerical information supplied by manufacturers unlike AI

Drone Flocking Optimization using NSGA-II and Principal Component Analysis

Individual agents in natural systems like flocks of birds or schools offish display a remarkable ability to coordinate and communicate in local groups and execute a variety of tasks efficiently. Emulating such natural systems into drone swarms to solve problems in defense, agriculture, industrial automation, and humanitarian relief is an emerging technology. However, flocking of aerial robots while maintaining multiple objectives, like collision avoidance, high speed etc., is still a challenge. This paper proposes optimized flocking of drones in a confined environment with multiple conflicting objectives. The considered objectives are collision avoidance (with each other and the wall), speed, correlation, and communication (connected and disconnected agents). Principal Component Analysis (PCA) is applied for dimensionality reduction and understanding the collective dynamics of the swarm. The control model is characterized by 12 parameters which are then optimized using a multi-objective solver (NSGA-II). The obtained results are reported and compared with that of the CMA-ES algorithm. The study is particularly useful as the proposed optimizer outputs a Pareto Front representing different types of swarms that can be applied to different scenarios in the real world

Multiple elite individual guided piecewise search-based differential evolution

The differential evolution (DE) algorithm relies mainly on mutation strategy and control parameters' selection. To take full advantage of top elite individuals in terms of fitness and success rates, a new mutation operator is proposed. The control parameters such as scale factor and crossover rate are tuned based on their success rates recorded over past evolutionary stages. The proposed DE variant, MIDE, performs the evolution in a piecewise manner, i.e., after every predefined evolutionary stages, MIDE adjusts its settings to enrich its diversity skills. The performance of the MIDE is validated on two different sets of benchmarks: CEC 2014 and CEC 2017 (special sessions & competitions on real-parameter single objective optimization) using different performance measures. In the end, MIDE is also applied to solve constrained engineering problems. The efficiency and effectiveness of the MIDE are further confirmed by a set of experiments.Agency for Science, Technology and Research (A*STAR)Submitted/Accepted versionThis work was supported by the A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund - Pre-Positioning (IAF-PP) (Award A19D6a0053