Intelligent Demand Forecasting and Replenishment System by Using Nature-Inspired Computing

The fashion apparel industry is too diverse, volatile and uncertain due to the fast changing market scenario. Forecasting demands of consumers has become survival necessity for organizations dealing with this field. Many traditional approaches have been proposed for improving the computational time and accuracy of the forecasting system. However, most of the approaches have over-looked the uncertainty existing in the fashion apparel market due to certain unpredictable events such as new trends, new promotions and advertisements, sudden rise and fall in economic conditions and so on. In this chapter, an intelligent multi-agent based demand forecasting and replenishment system has been proposed that adopts features from nature-inspired computing for handling uncertainty of the fashion apparel industry. The proposed system is inspired from the group hunting behaviour of crocodiles such as they form temporary alliances with other crocodiles for their own benefit even after being territorial creatures.

Particle Swarm Optimization for Cost Reduction in Mobile Location Management Using Reporting Cell Planning Approach

Location management is a very critical and intricate problem in wireless mobile communication which involves tracking the movement of the mobile users in the cellular network. Particle Swarm Optimization (PSO) is proposed for the optimal design of the cellular network using reporting cell planning (RCP) strategy. In this state-of-the-art approach, the proposed algorithm reduces the involved total cost such as location update and paging cost for the location management issue. The same technique is proved to be a competitive approach to different existing test network problems showing the efficacy of the proposed method through simulation results. The result obtained is also validated for real network data obtained from BSNL, Odisha. Particle Swarm Optimization is used to find the optimal set of reporting cells in a given cellular network by minimizing the location management cost. This RCP technique applied to this cost minimization problem has given improved result as compared to the results obtained in the previous literature.

Stellar Mass Black Hole for Engineering Optimization

In recent years, nature-inspired algorithms have been popular due to the fact that many real-world optimization problems are increasingly large, complex and dynamic. By reasons of the size and complexity of the problems, it is necessary to develop an optimization method whose efficiency is measured by finding the near optimal solution within a reasonable amount of time. A black hole is an object that has enough masses in a small enough volume that its gravitational force is strong enough to prevent light or anything else from escaping. Stellar mass Black hole Optimization (SBO) is a novel optimization algorithm inspired from the property of the gravity's relentless pull of black holes which are presented in the Universe. In this paper SBO algorithm is tested on benchmark optimization test functions and compared with the Cuckoo Search, Particle Swarm Optimization and Artificial Bee Colony systems. The experiment results show that the SBO outperforms the existing methods.

Recent Advances in Artificial Immune Systems

The designers of Artificial Immune Systems (AIS) had been inspired from the properties of natural immune systems: self-organization, adaptation and diversity, learning by continual exposure, knowledge extraction and generalization, clonal selection, networking and meta-dynamics, knowledge of self and non-self, etc. The aim of this chapter, along its sections, is to describe the principles of artificial immune systems, the most representational data structures (for the representation of antibodies and antigens), suitable metrics (which quantifies the interactions between components of the AIS) and their properties, AIS specific algorithms and their characteristics, some hybrid computational schemes (based on various soft computing methods and techniques like artificial neural networks, fuzzy and intuitionistic-fuzzy systems, evolutionary computation, and genetic algorithms), both standard and extended AIS models/architectures, and AIS applications, in the end.

Applying an Electromagnetism-Like Algorithm for Solving the Manufacturing Cell Design Problem

Group technology has acquired a great consideration in the last years. This technique allows including the advantages of serial production to any manufacturing industry by dividing a manufacturing plant into a set of machine-part cells. The identification and formation of the cells are known as the Manufacturing Cell Design Problem (MCDP), which is an NP-hard problem. In this paper, the authors propose to solve the problem through a swarm intelligence metaheuristic called ElectroMagnetism-like (EM-like) algorithm, which is inspired by the attraction-repulsion mechanism of particles in the context of the electromagnetic theory. The original EM-like algorithm was designed for solving continuous optimization problems, while the MCDP is usually formulated by assuming a binary approach. Hence, the authors propose an adaptation of this algorithm for addressing the problem. Such adaptation is applied for solving a freely available dataset of the MCDP, obtaining competitive results compared to recent approaches.

Role Allocation in a Group of Control Objects

The efficiency of control objects that fulfill economic and military tasks in groups depends on the correct role allocation among them. This paper develops a role allocation algorithm for control objects jointly fulfilling a collective task. Control objects are represented as intelligent agents with some capabilities and needs. The problem is solved by ranking the agents based on their closeness to given roles in terms of their functionality and characteristics. The efficiency of the suggested approach is illustrated by allocating the role of reconnaissance aircraft in the group attack problem with a protected target.

Collective-Animal-Behaviour-Based Optimized Null Placement in Time-Modulated Linear Antenna Arrays

In this paper optimal design of time modulated linear antenna arrays (TMLAA) with optimal placement of nulls in the desired direction of elevation plane has been dealt with the approach based on evolutionary algorithm like collective animal behaviour (CAB). Analysis has been done in theoretical and practical environment. Firstly the current excitation weights of the linear array of isotropic elements have been optimized by CAB is applied to improve null performance of TMLAA by Radio Frequency (RF) switch in MATLAB environment. The nulls positions of a TMLAA can be reduced significantly by optimizing the static excitation amplitudes and proper design of switch-on time intervals of each element. The CAB adjusts the current excitation amplitude of each element to place deeper nulls in the desired directions. Secondly the obtained optimal current excitation weight of the array factor is practically implemented in computer simulation technology- microwave studio (CST- MWS) environment. The array of microstrip patch antenna has been designed to operate at 5.85 GHz.

Harmony Search Algorithm

Harmony search (HS) is a meta-heuristic search algorithm which tries to mimic the improvisation process of musicians in finding a pleasing harmony. In recent years, due to some advantages, HS has received a significant attention. HS is easy to implement, converges quickly to the optimal solution and finds a good enough solution in a reasonable amount of computational time. The merits of HS algorithm have led to its application to optimization problems of different engineering areas. In this chapter, the concepts and performance of HS algorithm are shown and some engineering applications are reviewed. It is observed that HS has shown promising performance in solving difficult optimization problems and different versions of this algorithm have been developed. In the next years, it is expected that HS is applied to more real optimization problems.

Automatic Generation Control of Hydro-Hydro Interconnected Power System Based on Ant Colony Optimization

Each hydropower system incorporates with appropriate hydro turbine, and hydro governor unit. In the current work, an Automatic Generation Control (AGC) of two equal hydropower systems with Proportional-Integral-Derivative (PID) controller was investigated. The gain values of the PID controllers were tuned using Ant Colony Optimization (ACO) technique with one percent Step Load Perturbation (1% SLP) in area 1. The Integral Square Error (ISE), Integral Time Square Error (ITSE), Integral Absolute Error (IAE) and Integral Time Absolute Error (ITAE) were chosen as the objective function in order to optimize the controller's gain values. The experimental results reported that the IAE based PID controller improved the system performance compared to other objective functions during sudden load disturbance.

An Incremental Evolutionary Approach to Tabu Search

Based on recent viral transmission events in the swine species, we present a new framework to implement and execute tabu search (TS). The framework mimics the gradual evolutionary process observed when certain flu viruses move from one host population to another. It consists of three steps: (1) executing TS on a smaller subset of the original problem, (2) using one of its promising solutions as an initial solution for a marginally larger problem, and (3) repeating this process until the original problem is reached and solved. Numerical experiments conducted with randomly-generated vehicle routing instances demonstrate interesting results.