Recent Advances in Computational Optimization [electronic resource] /
edited by Stefka Fidanova.
- X, 183 p. online resource.
- Studies in Computational Intelligence, 470 1860-949X ; .
- Studies in Computational Intelligence, 470 .
Intuitionistic Fuzzy Logic as a Tool for Quality Assessment of Genetic Algorithms Performances -- A Graph Optimization Approach to Item-Based Collaborative Filtering -- Constraint Propagation for the Dial-a-Ride Problem with Split Loads -- ACO and GA for Parameter Settings of E.coli Fed-batch Cultivation Model -- A Heuristic Based Algorithm for the 2D Circular Strip Packing Problem -- Experimental Evaluation of Pheromone Structures for Ant Colony Optimization: Application to the Robot Skin Wiring Problem -- Homogeneous Non Idling Problems: Models and Algorithms -- Flow Models for Project Scheduling with Transfer Delays and Financial Constraints -- A New Hybrid GA-FA Tuning of PID Controller for Glucose Concentration Control -- Olympia Roeva and Tsonyo Slavov Constraints -- A New Hybrid GA-FA Tuning of PID Controller for Glucose Concentration Control -- Olympia Roeva and Tsonyo Slavov Concentration Control -- Olympia Roeva and Tsonyo Slavov Some properties of the Broyden restricted class of updates with oblique projections. .
Optimization is part of our everyday life. We try to organize our work in a better way and optimization occurs in minimizing time and cost or the maximization of the profit, quality and efficiency. Also many real world problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume is a comprehensive collection of extended contributions from the Workshop on Computational Optimization. This book presents recent advances in computational optimization. The volume includes important real world problems like parameter settings for con- trolling processes in bioreactor, robot skin wiring, strip packing, project scheduling, tuning of PID controller and so on. Some of them can be solved by applying traditional numerical methods, but others need a huge amount of computational resources. For them it is shown that is appropriate to develop algorithms based on metaheuristic methods like evolutionary computation, ant colony optimization, constrain programming etc.