6 research outputs found
Multiple Cooperative Swarms for Data Clustering
Exploring a set of unlabeled data to extract the similar clusters,
known as data clustering, is an appealing problem in machine
learning. In other words, data clustering organizes the underlying
data into different groups using a notion of similarity between
patterns.
A new approach to solve the data clustering problem based on
multiple cooperative swarms is introduced. The proposed approach is
inspired by the social swarming behavior of biological bird flocks
which search for food situated in several places. The proposed
approach is composed of two main phases, namely, initialization and
exploitation. In the initialization phase, the aim is to distribute
the search space among several swarms. That is, a part of the search
space is assigned to each swarm in this phase. In the exploitation
phase, each swarm searches for the center of its associated cluster
while cooperating with other swarms. The search proceeds to converge
to a near-optimal solution. As compared to the single swarm
clustering approach, the proposed multiple cooperative swarms
provide better solutions in terms of fitness function measure for
the cluster centers, as the dimensionality of data and number of
clusters increase.
The multiple cooperative swarms clustering approach assumes that the
number of clusters is known a priori. The notion of stability
analysis is proposed to extract the number of clusters for the
underlying data using multiple cooperative swarms. The mathematical
explanations demonstrating why the proposed approach leads to more
stable and robust results than those of the single swarm clustering
are also provided.
Application of the proposed multiple cooperative swarms clustering
is considered for one of the most challenging problems in speech
recognition: phoneme recognition. The proposed approach is used to
decompose the recognition task into a number of subtasks or modules.
Each module involves a set of similar phonemes known as a phoneme
family. Basically, the goal is to obtain the best solution for
phoneme families using the proposed multiple cooperative swarms
clustering. The experiments using the standard TIMIT corpus indicate
that using the proposed clustering approach boosts the accuracy of
the modular approach for phoneme recognition considerably
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Multi-Objective and Multi-Attribute Optimisation for Sustainable Development Decision Aiding
Optimization is considered as a decision-making process for getting the most out of available resources for the best attainable results. Many real-world problems are multi-objective or multi-attribute problems that naturally involve several competing objectives that need to be optimized simultaneously, while respecting some constraints or involving selection among feasible discrete alternatives. In this Reprint of the Special Issue, 19 research papers co-authored by 88 researchers from 14 different countries explore aspects of multi-objective or multi-attribute modeling and optimization in crisp or uncertain environments by suggesting multiple-attribute decision-making (MADM) and multi-objective decision-making (MODM) approaches. The papers elaborate upon the approaches of state-of-the-art case studies in selected areas of applications related to sustainable development decision aiding in engineering and management, including construction, transportation, infrastructure development, production, and organization management