Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Optimal Portfolio Management for Engineering Problems Using Nonconvex Cardinality Constraint: A Computing Perspective

The problem of portfolio management relates to the selection of optimal stocks, which results in a maximum return to the investor while minimizing the loss. Traditional approaches usually model the portfolio selection as a convex optimization problem and require the calculation of gradient. Note that gradient-based methods can stuck at local optimum for complex problems and the simplification of portfolio optimization to convex, and further solved using gradient-based methods, is at a high cost of solution accuracy. In this paper, we formulate a nonconvex model for the portfolio selection problem, which considers the transaction cost and cardinality constraint, thus better reflecting the decisive factor affecting the selection of portfolio in the real-world. Additionally, constraints are put into the objective function as penalty terms to enforce the restriction. Note that this reformulated problem cannot be readily solved by traditional methods based on gradient search due to its nonconvexity. Then, we apply the Beetle Antennae Search (BAS), a nature-inspired metaheuristic optimization algorithm capable of efficient global optimization, to solve the problem. We used a large real-world dataset containing historical stock prices to demonstrate the efficiency of the proposed algorithm in practical scenarios. Extensive experimental results are presented to further demonstrate the efficacy and scalability of the BAS algorithm. The comparative results are also performed using Particle Swarm Optimizer (PSO), Genetic Algorithm (GA), Pattern Search (PS), and gradient-based fmincon (interior-point search) as benchmarks. The comparison results show that the BAS algorithm is six times faster in the worst case (25 times in the best case) as compared to the rival algorithms while achieving the same level of performance

On Tackling Real-Life Optimization Problems

Most real-world applications are concerned with minimizing or maximizing some quantity so as to enhance some result. This emphasizes the importance of optimization and subsequently the significance of the optimization methods that are able to tackle these real-life optimization problems. There are a number of practical reasons for which traditional optimization and exhaustive algorithms cannot deal with a variety of these real-life optimization applications although there are numerous optimization problems that can benefit from applying these traditional optimization algorithms to handle them. Therefore, their is a need for propsong new optimization algorithms (such as nature inspired optimization methods) and optimize the capabilities of the existing ones (such as hybridization and parallelization) as well. This paper investigates the most recent optimization directions for dealing with the real-life optimization problems with an application to one of the most common and important optimization problems in a variety of financial fields and other fields which is the portfolio optimization problem since it is considered one of the most crucial problems in the modern financial management and has a variety of applications such as asset management and building strategic asset allocation. The computational results were got utilizing benchmark data from the OR library with the use of modern optimization algorithms. In addition, the article highlights the differences and similarities among the utilized optimization methods. In addition, recent advancements to the utilized optimization methods are highlighted

Dual Target Optimization of Two-Dimensional Truss Using Cost Efficiency and Structural Reliability Sufficiency

The main contribution of this study is to open a discussion regarding the structural optimization associated with the cost efficiency and structural reliability sufficiency consideration. To do so, several various optimization approaches are investigated to deliberate both cost and reliability concerns. Particularly, particle swarm optimization is highlighted as a reliable optimization approach. Accordingly, an illustrative example is rendered to compare the feasibility of the considered optimization approaches. The feasibility of the investigated approaches is evaluated using the cost and reliability analysis. For the considered example, it was observed that the PSO optimization algorithm has multiple advantages such as easy realization, fast convergence, and promising performance in nonlinear performance optimization. The PSO optimization algorithm can be successfully applied in various fields of civil engineering. This popularity is due to the understandable performance of the PSO as well as its simplicity. In this paper, first, the literature on the subject has been described by two-dimensional truss analysis using the finite element method and optimized using the PSO particle swarm algorithm. A comparison of the results with this reference indicates the accuracy of this particle swarm algorithm in truss optimization. Indeed, this study ignites two main insights in structural optimizations assessment. The first illustration is related to how to establish a framework for structural system reliability analysis associated with the different degrees of indeterminacies. And the second illustration is related to making a decision problem concerning the structural optimization while both cost and reliability metric are two main parameters for the construction point of the view