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The Dance Language and Orientation of Bees

Particle swarm optimization (PSO). A tutorial

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Nonlinear Functional Analysis And Its Applications

African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Metaheuristics play a critical role in solving optimization problems, and most of them have been inspired by the collective intelligence of natural organisms in nature. This paper proposes a new metaheuristic algorithm inspired by gorilla troops' social intelligence in nature, called Artificial Gorilla Troops Optimizer (GTO). In this algorithm, gorillas' collective life is mathematically formulated, and new mechanisms are designed to perform exploration and exploitation. To evaluate the GTO, we apply it to 52 standard benchmark functions and seven engineering problems. Friedman's test and Wilcoxon rank-sum statistical tests statistically compared the proposed method with several existing metaheuristics. The results demonstrate that the GTO performs better than comparative algorithms on most benchmark functions, particularly on high-dimensional problems. The results demonstrate that the GTO can provide superior results compared with other metaheuristics.

Artificial gorilla troops optimizer: A new nature-inspired metaheuristic algorithm for global optimization problems

Particle Swarm Optimization is a popular heuristic search algorithm which is inspired by the social learning of birds or fishes. It is a swarm intelligence technique for optimization developed by Eberhart and Kennedy [1] in 1995. Inertia weight is an important parameter in PSO, which significantly affects the convergence and exploration-exploitation trade-off in PSO process. Since inception of Inertia Weight in PSO, a large number of variations of Inertia Weight strategy have been proposed. In order to propose one or more than one Inertia Weight strategies which are efficient than others, this paper studies 15 relatively recent and popular Inertia Weight strategies and compares their performance on 05 optimization test problems.

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Inertia Weight strategies in Particle Swarm Optimization

Swarm intelligence is one of the most promising area for the researchers in the field of numerical optimization. Researchers have developed many algorithms by simulating the swarming behavior of various creatures like ants, honey bees, fish, birds and the findings are very motivating. In this paper, a new approach for numerical optimization is proposed by modeling the foraging behavior of spider monkeys. Spider monkeys have been categorized as fission–fusion social structure based animals. The animals which follow fission–fusion social systems, split themselves from large to smaller groups and vice-versa based on the scarcity or availability of food. The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.

Spider Monkey Optimization algorithm for numerical optimization

In recent years, swarm intelligence has proven its importance for the solution of those problems that cannot be easily dealt with classical mathematical techniques The foraging behaviour of honey bees produces an intelligent social behaviour and falls in the category of swarm intelligence Artificial bee colony ABC algorithm is a simulation of honey bee foraging behaviour, established by Karaboga in 2005 Since its inception, a lot of research has been carried out to make ABC more efficient and to apply it on different types of problems This paper presents a review on ABC developments, applications, comparative performance and future research perspectives

Artificial bee colony algorithm: a survey

Hybrid Artificial Bee Colony algorithm with Differential Evolution

Artificial Bee Colony (ABC) algorithm has been emerged as one of the latest Swarm Intelligence based algorithm. Though, ABC is a competitive algorithm as compared to many other optimization techniques, the drawbacks like preference on exploration at the cost of exploitation and skipping the true solution due to large step sizes, are also associated with it. In this paper, two modifications are proposed in the basic version of ABC to deal with these drawbacks: solution update strategy is modified by incorporating the role of fitness of the solutions and a local search based on greedy logarithmic decreasing step size is applied. The modified ABC is named as accelerating ABC with an adaptive local search (AABCLS). The former change is incorporated to guide to not so good solutions about the directions for position update, while the latter modification concentrates only on exploitation of the available information of the search space. To validate the performance of the proposed algorithm AABCLS, $$30$$
 benchmark optimization problems of different complexities are considered and results comparison section shows the clear superiority of the proposed modification over the Basic ABC and the other recent variants namely, Best-So-Far ABC (BSFABC), Gbest guided ABC (GABC), Opposition based levy flight ABC (OBLFABC) and Modified ABC (MABC).

Shimpi Singh Jadon

Papers

Inertia Weight strategies in Particle Swarm Optimization

Spider Monkey Optimization algorithm for numerical optimization

Artificial bee colony algorithm: a survey

Hybrid Artificial Bee Colony algorithm with Differential Evolution

Accelerating Artificial Bee Colony algorithm with adaptive local search