Guest Editors
Dr. Sandeep Kautish, LBEF Campus (Asia Pacific University of Technology & Innovation, Malaysia), Nepal.
Dr. Ali Wagdy Mohamed, Cairo University, Egypt & The American University in Cairo, Egypt.
Dr. Ahmed J. Obaid, University of Kufa, Iraq.
Dr Deepmala Singh, LBEF Campus (Asia Pacific University of Technology & Innovation, Malaysia), Nepal.
Prof. Sheng Lung Peng, National Taipei University of Business, Taiwan.
Summary
Bio-inspired computational intelligence and optimization techniques are a set of novel problem-solving methodologies and approaches and have been attracted wider attention for their good performance. Exemplary examples of nature-inspired algorithms include fuzzy systems (FS), artificial neural networks (ANN), evolutionary computing (EC), and swarm intelligence (SI), and they have been proved as very effective and efficient for solving many real-world problems. These techniques are referred to as bio-inspired optimization (BIO) algorithms. BIO can be referred to as a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines which attempt to study, model, and analyze very complex real-world problems and/or phenomena.
This special issue will provide a systematic overview and state-of-the-art and contemporary researches in the field of computational intelligence and optimization methods and their application to various engineering applications. The primary task of this special issue is on some hybrid algorithms for solving the real-world complex constrained optimization problems from engineering domain for modeling of various benchmark design problems and hence present an efficient technique using bio-inspired optimization. In light of this, the aim of this special issue is to provide a unified platform for bringing forth and advancing the state-of-the-art in latest developments of nature-inspired algorithms, such as genetic algorithm, particle swarm optimization, ant colony optimization, migrating birds optimization, neural networks, gravitational search algorithm, and their applications.
Researchers are invited to submit innovative works on the theory and implementation of this family of techniques, in order to provide an up-to-date overview on the field.
Keywords
Evolutionary Computations
Evolutionary Strategies/Programming
Genetic Algorithms/Programming
Artificial Neural Network
Hybrid algorithms
Flower Pollination Algorithm
Cat Swarm Optimization Algorithm
Applications of the bio-inspired algorithms
Particle swarm optimization
Firefly algorithm
Fireworks algorithm
Bees algorithm
Evolutionary algorithms
Neural networks
Deep learning
Soft computing methods
Nature-inspired heuristics
Fuzzy optimization
Constrained optimization
Published Papers
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