Adaptive multimodal continuous ant colony optimization

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization algorithms in preserving high diversity, this paper intends to extend ant colony optimization algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ant colony optimization algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima

Ant colony system-based applications to electrical distribution system optimization

Chapter 16, February 201

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization

Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with Lévy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system

Cost of quality and quality optimization in manufacturing

This article looks to find the optimization between the quality of manufactured products and the components of the costs of quality, including prevention cost, appraisal cost, internal failure cost and external failure cost. The results for all products, such as automobiles, appliances and electronic devices can be used. Any serious attempts to improve quality must take into account the costs associated with achieving quality, since nowadays it does not suffice to meet customer requirements, it must be done at the lowest possible cost as well. In the current business environment, management accountants and designers need tools and to develop models for decision making and planning with the reduction in quality costs of the products to provide products with high quality in order to create value for an organization. In this paper, to improve (optimize) the quality and components of the costs of quality, a kind of ant colony algorithm under the title of minimum and maximum ants system has been implemented. The current study has at least one aspect of innovation. On the international level, no research has been done in regards to the relationship between quality and the costs of quality. It was found that from a product differentiation point of view and a cost leadership point of view what component of a product best leads to the optimization of the relationship between cost of quality and quality.

Cost of quality and quality optimization in manufacturing

This article looks to find the optimization between the quality of manufactured products and the components of the costs of quality, including prevention cost, appraisal cost, internal failure cost and external failure cost. The results for all products, such as automobiles, appliances and electronic devices can be used. Any serious attempts to improve quality must take into account the costs associated with achieving quality, since nowadays it does not suffice to meet customer requirements, it must be done at the lowest possible cost as well. In the current business environment, management accountants and designers need tools and to develop models for decision making and planning with the reduction in quality costs of the products to provide products with high quality in order to create value for an organization. In this paper, to improve (optimize) the quality and components of the costs of quality, a kind of ant colony algorithm under the title of minimum and maximum ants system has been implemented. The current study has at least one aspect of innovation. On the international level, no research has been done in regards to the relationship between quality and the costs of quality. It was found that from a product differentiation point of view and a cost leadership point of view what component of a product best leads to the optimization of the relationship between cost of quality and quality.