Vassilios Vassiliadis

Experimental study on a hybrid nature-inspired algorithm for financial portfolio optimization

Hybrid intelligent schemes have proven their efficiency in solving NP-hard optimization problems Portfolio optimization refers to the problem of finding the optimal combination of assets and their corresponding weights which satisfies a specific investment goal and various constraints In this study, a hybrid intelligent metaheuristic, which combines the Ant Colony Optimization algorithm and the Firefly algorithm, is proposed in tackling a complex formulation of the portfolio management problem The objective function under consideration is the maximization of a financial ratio which combines factors of risk and return At the same time, a hard constraint, which refers to the tracking ability of the constructed portfolio towards a benchmark stock index, is imposed The aim of this computational study is twofold Firstly, the efficiency of the hybrid scheme is highlighted Secondly, comparison results between alternative mechanisms, which are incorporated in the main function of the hybrid scheme, are presented.

NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the systems parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.

Active Portfolio Management under a Downside Risk Framework: Comparison of a Hybrid Nature --- Inspired Scheme

Hybrid intelligent systems are becoming more and more popular in solving nondeterministic polynomial-time --- hard optimization problems. Lately, the focus is on nature --- inspired intelligent algorithms, whose main advantage is the exploitation of unique features of natural systems. One type of complex optimization problems is the active portfolio management, where the incorporation of complex, realistic constraints makes it difficult for traditional numerical methods to deal with it. In this paper we perform a computational study of a hybrid Ant Colony Optimization algorithm. The application is a specific formulation of the problem. Our main aim in this paper is to introduce a new framework of study in the field of active portfolio management, where the main interest lies in minimizing the risk of the portfolio return falling below the benchmark. Secondary, we provide some preliminary results regarding the use of a new hybrid nature --- inspired scheme in solving this type of problem.

Hybrid nature-inspired intelligence for the resource leveling problem

The paper deals with a class of problems often met in modern project management under the term “resource leveling optimization problems”. The problems of this kind refer to the optimal allocation of available resources in a candidate project and have emerged, as the result of the even increasing needs of project managers in facing project complexity, controlling related budgeting and finances and managing the construction production line. For the effective resolution of resource leveling optimization problems, the use of nature inspired intelligent methodologies is proposed. Traditional approaches, such as exhaustive or greedy search methodologies, often fail to provide near-optimum solutions in a short amount of time, whereas the proposed intelligent approaches manage to timely achieve high quality near-optimal solutions. In the paper, extensive experimental results are presented, based on available data collections existing in literature for a number of known benchmark project management problems. The comparative analysis of three different intelligent metaheuristics, shows that a hybrid nature inspired intelligent approach, combining ant colony optimization and genetic algorithms, proves to be the most effective approach in the majority of benchmark problems and special decision making settings tested.

Nature Inspired Intelligence for the Constrained Portfolio Optimization Problem

In this paper, we apply a basic Bee Colony Optimization algorithm in order to find a high-quality solution for the constrained portfolio optimization problem. Moreover, we use a basic Ant Colony Optimization algorithm and a Tabu Search metaheuristic approach as a benchmark. Our findings indicate that nature-inspired methodologies are able to find feasible solutions for dynamic optimization problems in a reasonable amount of time in contrast with the simple tabu search.

On the performance and convergence properties of hybrid intelligent schemes: application on portfolio optimization domain

Hybrid intelligent algorithms, especially those who combine natureinspired techniques, are well known for their searching abilities in complex problem domains and their performance. One of their main characteristic is that they manage to escape getting trapped in local optima. In this study, two hybrid intelligent schemes are compared both in terms of performance and convergence ability in a complex financial problem. Particularly, both algorithms use a type of genetic algorithm for asset selection and they differ on the technique applied for weight optimization: the first hybrid uses a numerical function optimization method, while the second one uses a continuous ant colony optimization algorithm. Results indicate that there is great potential in combining characteristics of natureinspired algorithms in order to solve NP-hard optimization problems.

Stochastic Convergence Analysis of Metaheuristic Optimisation Techniques

Commonly used metaheuristic optimisation techniques imbed stochastic elements into the selection of the initial population or/and into the solution-search strategy. Introducing randomness is often a means of escaping from local optima when searching for the global solution. However, depending on the ruggedness of the optimisation landscape and the complexity of the problem at hand, this practice leads to a dispersion of the reported solutions. Instead of relying on the best solution found in a set of runs, as is typical in many optimisation exercises, it is essential to get an indication of the expected dispersion of results by estimating the probability of converging to a “good” solution after a certain number of generations. We apply a range of statistical techniques for estimating the success probability and the convergence rate of popular evolutionary optimisation heuristics in the context of portfolio management. We show how this information can be utilised by a researcher to obtain a deeper understanding of algorithmic behaviour and to evaluate the relative performance of competitive optimisation schemes.

Algorithms and Methods Inspired from Nature for Solving Supply Chain and Logistics Optimization Problems: A Survey

The current work surveys 245 papers and research reports related to algorithms and methods inspired from nature for solving supply chain and logistics optimization problems. Nature Inspired Intelligence (NII) is a challenging new subfield of artificial intelligence (AI) particularly capable of dealing with complex optimization problems. Related approaches are used either as stand-alone algorithms, or as hybrid schemes i.e. in combination to other AI techniques. Ant Colony Optimization (ACO), Particle Swarm Optimization, Artificial Bee Colonies, Artificial Immune Systems and DNA Computing are some of the most popular approaches belonging to nature inspired intelligence. On the other hand, supply chain management represents an interesting domain of OR applications, including a variety of hard optimization problems such as vehicle routing (VRP), travelling salesman (TSP), team orienteering, inventory, knapsack, supply network problems, etc. Nature inspired intelligent algorithms prove capable of identifying near optimal solutions for instances of those problems with high degree of complexity in a reasonable amount of time. Survey findings indicate that NII can cope successfully with almost any kind of supply chain optimization problem and tends to become a standard in related scientific literature during the last five years.

Parameter tuning of hybrid nature-inspired intelligent metaheuristics for solving financial portfolio optimization problems

In previous studies, nature-inspired algorithms have been implemented in order to tackle hard NP-optimization problems, in the financial domain. Specifically, the task of finding optimal combination of assets with the aim of efficiently allocating your available capital is of major concern. One of the main reasons, which justifies the difficulties entailed in this problem, is the high level of uncertainty in the financial markets and not only. As mentioned above, artificial intelligent algorithms may provide a solution to this task. However, there is one major drawback concerning these techniques: the large number of open parameters. The aim of this study is twofold. Firstly, results from extended simulations are presented regarding the application of a specific hybrid nature-inspired metaheuristic in a particular formulation of the financial portfolio optimization problem. The main focus is on presenting comparative results regarding the performance of the proposed scheme for various configuration settings. Secondly, it is our intend to enhance the hybrid schemes performance by incorporating intelligent searching components such as other metaheuristics (simulated annealing).

A Novel Hybrid Nature-Inspired Scheme for Solving a Financial Optimization Problem

Hybrid intelligent approaches have proven their potential in demanding problem settings. The financial domain provides some challenging problem set-ups, mostly because of non-linearity conditions and conflicting objectives and binding restrictions. In this study, a novel hybrid algorithm, which stems from Nature-Inspired Intelligence, is applied in a specific portfolio optimization problem. The proposed algorithm comprises of an Ant Colony Optimization Algorithm (ACO) for detecting optimal combination of assets and a Gravitational Search Algorithm (GSA), for optimal capital allocation in the portfolio. Results from the proposed hybrid scheme are compared to previous findings, in the same optimization problem and dataset, from another hybrid NII algorithm, namely ACO Algorithm with Firefly Algorithm (FA). Experimental findings indicate that the proposed hybrid scheme yields a promising distribution of fitness values from independent simulation runs. What is more, in terms of best solution found, the proposed hybrid scheme yielded a solution that is 7.2% worst than the benchmark approach’s one. However, in terms of execution time, the proposed algorithm was faster. Taking into consideration both the above aspects, the difference of the two hybrid algorithms, in terms of best solution, can be characterized as insignificant. The main aim of the paper is to highlight the advantages of the proposed hybrid scheme, as well as the great potential of Nature-Inspired Intelligent algorithms for the financial portfolio optimization problem.