A general framework of multi-population methods with clustering in undetectable dynamic environments

Copyright @ 2011 IEEETo solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different sub-areas in the fitness landscape. Many experimental studies have shown that locating and tracking multiple relatively good optima rather than a single global optimum is an effective idea in dynamic environments. However, several challenges need to be addressed when multi-population methods are applied, e.g., how to create multiple populations, how to maintain them in different sub-areas, and how to deal with the situation where changes can not be detected or predicted. To address these issues, this paper investigates a hierarchical clustering method to locate and track multiple optima for dynamic optimization problems. To deal with undetectable dynamic environments, this paper applies the random immigrants method without change detection based on a mechanism that can automatically reduce redundant individuals in the search space throughout the run. These methods are implemented into several research areas, including particle swarm optimization, genetic algorithm, and differential evolution. An experimental study is conducted based on the moving peaks benchmark to test the performance with several other algorithms from the literature. The experimental results show the efficiency of the clustering method for locating and tracking multiple optima in comparison with other algorithms based on multi-population methods on the moving peaks benchmark

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

AbstractThe main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test

Integration of software reliability into systems reliability optimization

Reliability optimization originally developed for hardware systems is extended to incorporate software into an integrated system reliability optimization. This hardware-software reliability optimization problem is formulated into a mixed-integer programming problem. The integer variables are the number of redundancies, while the real variables are the components reliabilities;To search a common framework under which hardware systems and software systems can be combined, a review and classification of existing software reliability models is conducted. A software redundancy model with common-cause failure is developed to represent the objective function. This model includes hardware redundancy with independent failure as a special case. A software reliability-cost function is then derived based on a binomial-type software reliability model to represent the constraint function;Two techniques, the combination of heuristic redundancy method with sequential search method, and the Lagrange multiplier method with the branch-and-bound method, are proposed to solve this mixed-integer reliability optimization problem. The relative merits of four major heuristic redundancy methods and two sequential search methods are investigated through a simulation study. The results indicate that the sequential search method is a dominating factor of the combination method. Comparison of the two proposed mixed-integer programming techniques is also studied by solving two numerical problems, a series system with linear constraints and a bridge system with nonlinear constraints. The Lagrange multiplier method with the branch-and-bound method has been shown to be superior to all other existing methods in obtaining the optimal solution;Finally an illustration is performed for integrating software reliability model into systems reliability optimization

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Copyright © 2018 John Wiley & Sons, Ltd. This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110-unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well-organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems