Heuristic-based firefly algorithm for bound constrained nonlinear binary optimization

Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper,we address the practical testing of aheuristic-based FA (HBFA) for computing optimaof discrete nonlinear optimization problems,where the discrete variables are of binary type. An important issue in FA is the formulation of attractiveness of each firefly which in turn affects its movement in the search space. Dynamic updating schemes are proposed for two parameters, one from the attractiveness term and the other from the randomization term. Three simple heuristics capable of transforming real continuous variables into binary ones are analyzed. A new sigmoid ‘erf’ function is proposed. In the context of FA, three different implementations to incorporate the heuristics for binary variables into the algorithm are proposed. Based on a set of benchmark problems, a comparison is carried out with other binary dealing metaheuristics. The results demonstrate that the proposed HBFA is efficient and outperforms binary versions of differential evolution (DE) and particle swarm optimization (PSO). The HBFA also compares very favorably with angle modulated version of DE and PSO. It is shown that the variant of HBFA based on the sigmoid ‘erf’ function with ‘movements in continuous space’ is the best, both in terms of computational requirements and accuracy.Fundação para a Ciência e a Tecnologia (FCT

A Binary differential search algorithm for the 0-1 multidimensional knapsack problem

The multidimensional knapsack problem (MKP) is known to be NP-hard in operations research and it has a wide range of applications in engineering and management. In this study, we propose a binary differential search method to solve 0-1 MKPs where the stochastic search is guided by a Brownian motion-like random walk. Our proposed method comprises two main operations: discrete solution generation and feasible solution production. Discrete solutions are generated by integrating Brownian motion-like random search with an integer-rounding operation. However, the rounded discrete variables may violate the constraints. Thus, a feasible solution production strategy is used to maintain the feasibility of the rounded discrete variables. To demonstrate the efficiency of our proposed algorithm, we solved various 0-1 MKPs using our proposed algorithm as well as some existing meta-heuristic methods. The numerical results obtained demonstrated that our algorithm performs better than existing meta-heuristic methods. Furthermore, our algorithm has the capacity to solve large-scale 0-1 MKPs

Performance comparison of various probability gate assisted binary lightning search algorithm

Recently, many new nature-inspired optimization algorithms have been introduced to further enhance the computational intelligence optimization algorithms. Among them, lightning search algorithm (LSA) is a recent heuristic optimization method for resolving continuous problems. It mimics the natural phenomenon of lightning to find out the global optimal solution around the search space. In this paper, a suitable technique to formulate binary version of lightning search algorithm (BLSA) is presented. Three common probability transfer functions, namely, logistic sigmoid, tangent hyperbolic sigmoid and quantum bit rotating gate are investigated to be utilized in the original LSA. The performances of three transfer functions based BLSA is evaluated using various standard functions with different features and the results are compared with other four famous heuristic optimization techniques. The comparative study clearly reveals that tangent hyperbolic transfer function is the most suitable function that can be utilized in the binary version of LSA

Application of Pigeon Inspired Optimization for Multidimensional Knapsack Problem

The multidimensional knapsack problem (MKP) is a generalization of the classical knapsack problem, a problem for allocating a resource by selecting a subset of objects that seek for the highest profit while satisfying the capacity of knapsack constraint. The MKP have many practical applications in different areas and classified as a NP-hard problem. An exact method like branch and bound and dynamic programming can solve the problem, but its time computation increases exponentially with the size of the problem. Whereas some approximation method has been developed to produce a near-optimal solution within reasonable computational times. In this paper a pigeon inspired optimization (PIO) is proposed for solving MKP. PIO is one of the metaheuristic algorithms that is classified in population-based swarm intelligent that is developed based on the behavior of the pigeon to find its home although it had gone far away from it home. In this paper, PIO implementation to solve MKP is applied to two different characteristic cases in total 10 cases. The result of the implementation of the two-best combination of parameter values for 10 cases compared to particle swarm optimization, intelligent water drop algorithm and the genetic algorithm gives satisfactory results

Firefly Algorithm: Recent Advances and Applications

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.Comment: 15 page

Short-term scheduling of hybrid thermal, pumped-storage, and wind plants using firefly optimization algorithm

This paper presents a novel method based on an enhanced firefly algorithm (EFA) to solve scheduling hybrid thermal, pumped-storage, and wind plants. Since the scheduling problem is inherently discrete, basic EFA and binary encoding/decoding techniques are used in the proposed EFA approach. Optimal power values of thermal and pumped-storage units are determined separately in the presence of uncertainty caused by wind speed. The proposed method is applied to a real plant, including four pumped-storage units, 34 thermal units with different characteristics, and one wind turbine plant. In addition, dynamic constraints of upstream and downstream sources and constraints regarding thermal and wind units are also considered for finding the optimal solution. In addition, the proposed EFA is successfully applied to a real plant, and the results are compared with those of the three available methods. The results show that the proposed method has converted to a more optimal cost than the other methods

Facial Expression Recognition Using Uniform Local Binary Pattern with Improved Firefly Feature Selection

Facial expressions are essential communication tools in our daily life. In this paper, the uniform local binary pattern is employed to extract features from the face. However, this feature representation is very high in dimensionality. The high dimensionality would not only affect the recognition accuracy but also can impose computational constraints. Hence, to reduce the dimensionality of the feature vector, the firefly algorithm is used to select the optimal subset that leads to better classification accuracy. However, the standard firefly algorithm suffers from the risk of being trapped in local optima after a certain number of generations. Hence, this limitation has been addressed by proposing an improved version of the firefly where the great deluge algorithm (GDA) has been integrated. The great deluge is a local search algorithm that helps to enhance the exploitation ability of the firefly algorithm, thus preventing being trapped in local optima. The improved firefly algorithm has been employed in a facial expression system. Experimental results using the Japanese female facial expression database show that the proposed approach yielded good classification accuracy compared to state-of-the-art methods. The best classification accuracy obtained by the proposed method is 96.7% with 1230 selected features, whereas, Gabor-SRC method achieved 97.6% with 2560 features