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In this paper, a swarm intelligence optimization algorithm is proposed as the Shrike Optimization Algorithm (SHOA). Many creatures living in a group and surviving for the next generation randomly search for food; they follow the best one in…
This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient…
We introduced the Scorpion Hunting Strategy (SHS), a novel population-based, nature-inspired optimisation algorithm. This algorithm draws inspiration from the hunting strategy of scorpions, which identify, locate, and capture their prey…
This paper presents an in-depth survey and performance evaluation of the Cat Swarm Optimization (CSO) Algorithm. CSO is a robust and powerful metaheuristic swarm-based optimization approach that has received very positive feedback since its…
Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this…
A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm (DSA), is proposed in this study, which is inspired by the searching for food sources and foraging behaviors of the duck swarm. Two rules are modeled from the…
A novel meta-heuristic algorithm, Egret Swarm Optimization Algorithm (ESOA), is proposed in this paper, which is inspired by two egret species' (Great Egret and Snowy Egret) hunting behavior. ESOA consists of three primary components:…
The Whale Optimization Algorithm (WOA) has shown strong optimization ability but still suffers from premature convergence and weak search diversity. To address these issues, this paper proposes an enhanced WOA variant called CICDWOA. The…
In this paper, a new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The algorithm called artificial orca algorithm (AOA) consists of simulating the orca lifestyle and in particular the social…
Swarm Intelligence is a metaheuristic optimization approach that has become very predominant over the last few decades. These algorithms are inspired by animals' physical behaviors and their evolutionary perceptions. The simplicity of these…
This paper presents the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive foraging, strategic movement, and parasite avoidance behaviors.GOA is designed to balance…
Swarm intelligence algorithms have traditionally been designed for continuous optimization problems, and these algorithms have been modified and extended for application to discrete optimization problems. Notably, their application in…
Sea Horse Optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic…
Swarm optimization algorithms are widely used for feature selection before data mining and machine learning applications. The metaheuristic nature-inspired feature selection approaches are used for single-objective optimization tasks,…
Swarm intelligence optimization algorithms can be adopted in swarm robotics for target searching tasks in a 2-D or 3-D space by treating the target signal strength as fitness values. Many current works in the literature have achieved good…
Nature has long inspired the development of swarm intelligence (SI), a key branch of artificial intelligence that models collective behaviors observed in biological systems for solving complex optimization problems. Particle swarm…
Metaheuristic algorithms have gained widespread application across various fields owing to their ability to generate diverse solutions. One such algorithm is the Snake Optimizer (SO), a progressive optimization approach. However, SO suffers…
Nature-inspired swarm-based algorithms have been widely applied to tackle high-dimensional and complex optimization problems across many disciplines. They are general purpose optimization algorithms, easy to use and implement, flexible and…
Achieving better exploitation and exploration capabilities (EEC) have always been an important yet challenging issue in the design of evolutionary optimization algorithm (EOA). The difficulties lie in obtaining a good balance in EEC, which…
This paper presents innovative approaches to optimization problems, focusing on both Single-Objective Multi-Modal Optimization (SOMMOP) and Multi-Objective Optimization (MOO). In SOMMOP, we integrate chaotic evolution with niching…