Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems

http://osp.mans.edu.eg/elbeltagi/AI%20Comparison%205EAs.pdf

Comparison among five evolutionary-based optimization algorithms

A novel numerical optimization algorithm inspired from weed colonization

A memetic meta-heuristic called the shuffled frog-leaping algorithm (SFLA) has been developed for solving combinatorial optimization problems. The SFLA is a population-based cooperative search metaphor inspired by natural memetics. The algorithm contains elements of local search and global information exchange. The SFLA consists of a set of interacting virtual population of frogs partitioned into different memeplexes. The virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evolution. The algorithm performs simultaneously an independent local search in each memeplex. The local search is completed using a particle swarm optimization-like method adapted for discrete problems but emphasizing a local search. To ensure global exploration, the virtual frogs are periodically shuffled and reorganized into new memplexes in a technique similar to that used in the shuffled complex evolution algorithm. In addition, to provide the opportunity for random generation of improved information,...

Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

Shuffled Frog Leaping Algorithm (SFLA) is a meta-heuristic for solving discrete optimization problems. Here it is applied to determine optimal discrete pipe sizes for new pipe networks and for network expansions. SFLA is a population based, cooperative search metaphor inspired by natural memetics. The algorithm uses memetic evolution in the form of infection of ideas from one individual to another in a local search. The local search is similar in concept to particle swarm optimization. A shuffling strategy allows for the exchange of information between local searches to move toward a global optimum. This paper summarizes the development of SFLANET, a computer model that links SFLA and the hydraulic simulation software EPANET and its library functions. Application of SFLANET to literature network design problems is then described. Although the algorithm is in its initial stages of development, promising results were obtained.

Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm

Determining pump operations using particle swarm optimization

In water limited areas as water demand increases alternative sustainable water sources must be identified. One supply augmentation practice, that is already being applied in the arid southwest U.S., is artificial groundwater recharge usingwastewater effluent. The objective of a recharge facility is to supplement the available groundwater resources by storing water for the future. The resulting reclaimed water is used primarily for non-potable purposes but under increasing stressesshifting to potable use is likely to happen. Water quality thenbecomes a more pressing concern. Water quality improvements during infiltration and groundwater transport are significant and are collectively described as soil-aquifer treatment (SAT). To meet user needs, the recharge operation must be efficiently managed considering monetary, water quality and environmental concerns. In this paper, a SAT management model is developed that considers all of these concerns. Within the SAT management model, the shuffled complex evolution algorithm (SCE) is used as the optimization tool. SCEis a relatively new meta-heuristic search technique for continuousproblems that has been used extensively for hydrologic model calibration. In this application, SCE is integrated with the simulation models (MODFLOW, MT3D, and MODPATH) to represent movement and quality transformations. Two steady state case studies on a general hypothetical aquifer (modeled after a field site) were examined using the management model.

Optimal operation of artificial groundwater recharge systems considering water quality transformations

Water distribution networks are a significant investment. As such, a large volume of research has examined the pipe design/rehabilitation problem and is summarized in other papers. This paper focuses on the application of a new optimization method to the pipe sizing problem. In recent years, the researchers have attempted to exploit expanding computer power and combined new optimization techniques with hydraulic simulation software. The computer model in this work, SFLANET, is based upon the shuffled frog leaping algorithm (SFLA), a memetic algorithm (a kind of meta-heuristic). The optimization algorithm is linked to EPANET via the EPANET Toolkit and can be used to design large, complex pipe network systems. Here results are shown for the New York City Tunnel problem.

Muzaffar Eusuff

Papers

Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm

Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization

Determining pump operations using particle swarm optimization

Optimal operation of artificial groundwater recharge systems considering water quality transformations

Water distribution network design using the shuffled frog leaping algorithm