The application of water cycle algorithm to portfolio selection

Portfolio selection is one of the most vital financial problems in literature. The studied problem is a nonlinear multi-objective problem which has been solved by a variety of heuristic and metaheuristic techniques. In this article, a metaheuristic optimiser, the multiobjective water cycle algorithm (MOWCA), is represented to find efficient frontiers associated with the standard mean-variance (MV) portfolio optimisation model. The inspired concept of WCA is based on the simulation of water cycle process in the nature. Computational results are obtained for analyses of daily data for the period January 2012 to December 2014, including S&P100 in the US, Hang Seng in Hong Kong, FTSE100 in the UK, and DAX100 in Germany. The performance of the MOWCA for solving portfolio optimisation problems has been evaluated in comparison with other multi-objective optimisers including the NSGA-II and multiobjective particle swarm optimisation (MOPSO). Four well-known performance metrics are used to compare the reported optimisers. Statistical optimisation results indicate that the applied MOWCA is an efficient and practical optimiser compared with the other methods for handling portfolio optimisation problems

Application Of Water Cycle Algorithm For Optimal Cost Design Of Water Distribution Systems

Water distribution system (WDS) design is considered as a class of large combinatorial non-linear optimization problems having complex implicit constraints such as conservation of mass and energy equations. Due to the complexity and large feasible solution, traditional optimization techniques are not capable to tackle these kinds of problems. Recently, applications of metaheuristic algorithms, due to their efficiencies and performances, are increased dramatically. In this paper, water cycle algorithm (WCA), a recently developed population-based algorithm, coupled with hydraulic simulator, EPANET, are applied for finding the optimal cost design of WDS. The performance of the WCA is shown using well-known Balerma benchmark problem widely used in the literature. The obtained optimization results using the WCA are compared with other optimizers such as genetic algorithm, simulated annealing, and harmony search. Comparisons of obtained statistical results show the superiority of the WCA over other optimization methods in terms of convergence rate and solution quality

An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

Swarm intelligence is a relatively recent approach for solving optimization problems that usually adopts the social behavior of birds and animals. The most popular class of swarm intelligence is ant colony optimization (ACO), which simulates the behavior of ants in seeking and moving food. This chapter aim to briefly overview the important role of ant colony optimization methods in solving optimization problems in time-varying and dynamic environments. To this end, we describe concisely the dynamic optimization problems, challenges, methods, benchmarks, measures, and a brief review of methodologies designed using the ACO and its variants. Finally, a short bibliometric analysis is given for the ACO and its variants for solving dynamic optimization problems

Optimal Pipe Size Design for Looped Irrigation Water Supply System Using Harmony Search: Saemangeum Project Area

Water supply systems are mainly classified into branched and looped network systems. The main difference between these two systems is that, in a branched network system, the flow within each pipe is a known value, whereas in a looped network system, the flow in each pipe is considered an unknown value. Therefore, an analysis of a looped network system is a more complex task. This study aims to develop a technique for estimating the optimal pipe diameter for a looped agricultural irrigation water supply system using a harmony search algorithm, which is an optimization technique. This study mainly serves two purposes. The first is to develop an algorithm and a program for estimating a cost-effective pipe diameter for agricultural irrigation water supply systems using optimization techniques. The second is to validate the developed program by applying the proposed optimized cost-effective pipe diameter to an actual study region (Saemangeum project area, zone 6). The results suggest that the optimal design program, which applies an optimization theory and enhances user convenience, can be effectively applied for the real systems of a looped agricultural irrigation water supply

Optimal Pipe Size Design for Looped Irrigation Water Supply System Using Harmony Search: Saemangeum Project Area

Water supply systems are mainly classified into branched and looped network systems. The main difference between these two systems is that, in a branched network system, the flow within each pipe is a known value, whereas in a looped network system, the flow in each pipe is considered an unknown value. Therefore, an analysis of a looped network system is a more complex task. This study aims to develop a technique for estimating the optimal pipe diameter for a looped agricultural irrigation water supply system using a harmony search algorithm, which is an optimization technique. This study mainly serves two purposes. The first is to develop an algorithm and a program for estimating a cost-effective pipe diameter for agricultural irrigation water supply systems using optimization techniques. The second is to validate the developed program by applying the proposed optimized cost-effective pipe diameter to an actual study region (Saemangeum project area, zone 6). The results suggest that the optimal design program, which applies an optimization theory and enhances user convenience, can be effectively applied for the real systems of a looped agricultural irrigation water supply

Development and applications of metaheuristic algorithms in engineering design and structural optimization / Ali Sadollah

Metaheuristic algorithms have been extensively used in numerous domains especially in engineering. The reason is that for solving complex optimization problems, classical and traditional techniques may not efficiently find global optimum solution. In this thesis, the applications of a number of well-known metaheuristic algorithms for solving engineering problems have been considered. In addition, two novel optimization methods are developed and presented which are named the mine blast algorithm (MBA) and the water cycle algorithm (WCA). The fundamental concepts and ideas for MBA are derived from the explosion of mine bombs in real world. Accordingly, the ideas and philosophy of WCA are inspired from water cycle process in the nature and how rivers and streams flow to the sea in the real world. The efficiency of the proposed optimizers was evaluated using numerous well-known unconstrained and constrained benchmark functions which have been widely used in literature. Optimization of several truss structures (2D and 3D) with discrete variables were carried out using the proposed methods and the results and computational performances were compared with several well-known metaheuristic algorithms. The obtained optimization results shows that the proposed new metaheuristic algorithms are capable of offering faster convergence rate in addition to offering better optimal solutions compared to other optimizers. Furthermore, a comparative study was carried out to show the effectiveness of the proposed algorithms over other well-known methods in terms of computational time (speed) and function values. As an illustration of statistical optimization results, the MBA and WCA offer minimum weight of 27,532.95 and 29,304.76, respectively, for the complex 200-bar truss in less number of function evaluations (computational time) compared with other optimizers in the literature

How do artificial neural networks lead to developing an optimization method?

This concise paper explains the inspiration of AI particularly artificial neural networks (ANNs) for developing new metaheuristics. Using the unique concept of ANNs and its wide applications in different fields of study, how the ANNs can be utilized for solving real life and complex optimization problems? This paper briefly links the inspiration to a practical model in order to build an optimization strategy.</p