6 research outputs found
Social Emotional Optimization Algorithm for Solving Optimal Reactive Power Dispatch Problem
The main feature of solving Optimal Reactive Power Dispatch Problem (ORPD) is to minimize the real power loss and also to keep the voltage profile within the specified limits. Human society is a complex group which is more effective than other animal groups. Therefore, if one algorithm mimics the human society, the effectiveness maybe more robust than other swarm intelligent algorithms which are inspired by other animal groups. So in this paper Social Emotional Optimization Algorithm (SEOA) has been utilized to solve ORPD problem. The proposed algorithm (SEOA) has been validated, by applying it on standard IEEE 30 bus test system. The results have been compared to other heuristics methods and the proposed algorithm converges to best solution
Diminution of Active Power Loss by Communal Expressive Optimization Algorithm
Human society is a multifarious collection which is more effectual than other animal groups. Consequently, if one algorithm imitates the human society, then the efficiency may be healthier than other swarm intelligent algorithms which are stimulated by other animal groups. In this paper Communal Expressive (CE) Optimization Algorithm has been utilized to solve reactive power problem. The key feature of solving Optimal Reactive Power Problem is to reduce the real power loss and to maintain the voltage profile within the specified limits. The proposed Communal Expressive (CE) Optimization Algorithm has been authenticated, by applying it in standard IEEE 118 & practical 191 bus test systems. The results have been compared to other standard methods and the projected algorithm converges to finest solution
A Novel Human-Based Meta-Heuristic Algorithm: Dragon Boat Optimization
(Aim) Dragon Boat Racing, a popular aquatic folklore team sport, is
traditionally held during the Dragon Boat Festival. Inspired by this event, we
propose a novel human-based meta-heuristic algorithm called dragon boat
optimization (DBO) in this paper. (Method) It models the unique behaviors of
each crew member on the dragon boat during the race by introducing social
psychology mechanisms (social loafing, social incentive). Throughout this
process, the focus is on the interaction and collaboration among the crew
members, as well as their decision-making in different situations. During each
iteration, DBO implements different state updating strategies. By modelling the
crew's behavior and adjusting the state updating strategies, DBO is able to
maintain high-performance efficiency. (Results) We have tested the DBO
algorithm with 29 mathematical optimization problems and 2 structural design
problems. (Conclusion) The experimental results demonstrate that DBO is
competitive with state-of-the-art meta-heuristic algorithms as well as
conventional methods
Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations
In recent years, a great variety of nature- and bio-inspired algorithms has
been reported in the literature. This algorithmic family simulates different
biological processes observed in Nature in order to efficiently address complex
optimization problems. In the last years the number of bio-inspired
optimization approaches in literature has grown considerably, reaching
unprecedented levels that dark the future prospects of this field of research.
This paper addresses this problem by proposing two comprehensive,
principle-based taxonomies that allow researchers to organize existing and
future algorithmic developments into well-defined categories, considering two
different criteria: the source of inspiration and the behavior of each
algorithm. Using these taxonomies we review more than three hundred
publications dealing with nature-inspired and bio-inspired algorithms, and
proposals falling within each of these categories are examined, leading to a
critical summary of design trends and similarities between them, and the
identification of the most similar classical algorithm for each reviewed paper.
From our analysis we conclude that a poor relationship is often found between
the natural inspiration of an algorithm and its behavior. Furthermore,
similarities in terms of behavior between different algorithms are greater than
what is claimed in their public disclosure: specifically, we show that more
than one-third of the reviewed bio-inspired solvers are versions of classical
algorithms. Grounded on the conclusions of our critical analysis, we give
several recommendations and points of improvement for better methodological
practices in this active and growing research field.Comment: 76 pages, 6 figure