Firefly Mating Algorithm for Continuous Optimization Problems

Research on Economic Optimization of Microgrid Cluster Based on Chaos Sparrow Search Algorithm

With the deepening of the power market reform on the retail side, it is of great significance to study the economic optimization of the microgrid cluster system. Aiming at the economics of the microgrid cluster, comprehensively considering the degradation cost of energy storage battery, the compensation cost of demand-side controllable loads dispatch, the electricity transaction cost between the microgrids, and the electricity transaction cost between the microgrid and the power distribution network of the microgrid cluster, we establish an optimal dispatch model for the microgrid cluster including wind turbines, photovoltaics, and energy storage batteries. At the same time, in view of the problem that the population diversity of the basic sparrow search algorithm decreases and it is easy to fall into local extremes in the later iterations of the basic sparrow search algorithm, a chaos sparrow search algorithm based on Bernoulli chaotic mapping, dynamic adaptive weighting, Cauchy mutation, and reverse learning is proposed, and different types of test functions are used to analyze the convergence effect of the algorithm, and the calculation effects of the sparrow algorithm, the particle swarm algorithm, the chaotic particle swarm, and the genetic algorithm are compared. The algorithm has higher convergence speed, higher accuracy, and better global optimization ability. Finally, through the calculation example, it is concluded that the benefit of the microgrid cluster is increased by nearly 20%, which verifies the effectiveness of the improvement.

Firefly-Based Approaches of Image Recognition

The main aim of the reported work is to solve the registration problem for recognition purposes. We introduce two new evolutionary algorithms (EA) consisting of population-based search methods, followed by or combined with a local search scheme. We used a variant of the Firefly algorithm to conduct the population-based search, while the local exploration was implemented by the Two-Membered Evolutionary Strategy (2M-ES). Both algorithms use fitness function based on mutual information (MI) to direct the exploration toward an appropriate candidate solution. A good similarity measure is the one that enables us to predict well, and with the symmetric MI we tie similarity between two objects A and B directly to how well A predicts B, and vice versa. Since the search landscape of normalized mutual information proved more amenable for evolutionary computation algorithms than simple MI, we use normalized mutual information (NMI) defined as symmetric uncertainty. The proposed algorithms are tested against the well-known Principal Axes Transformation technique (PAT), a standard evolutionary strategy and a version of the Firefly algorithm developed to align images. The accuracy and the efficiency of the proposed algorithms are experimentally confirmed by our tests, both methods being excellently fitted to registering images.

An Enhanced Lightning Attachment Procedure Optimization with Quasi-Opposition-Based Learning and Dimensional Search Strategies

Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.