# A Novel Swarm Optimisation Algorithm Based on a Mixed-Distribution Model
## 1 Introduction  
Swarm intelligence was first proposed by [PERSON] and [PERSON] of the University of California in 1989. It was used in cell robots, and focuses on the behaviour and control of groups based on bio-intelligence theory and methods [1]. In the early 1990s, swarm intelligence algorithms were proposed that simulate the cooperative behaviour of groups of social animals. The core concept of swarm intelligence is that swarms composed of simple individuals can achieve more complex functions simply by cooperating with each other. Therefore, it is possible to solve complex distributed problems using swarm intelligence, which works on the premise of there being no centralised control and no global information or models. Unlike the classical optimization algorithm using deterministic rules, the swarm intelligence algorithm lets individuals search in the solution space simultaneously by using the probability transfer mode and a variety of random factors combined with metaheuristic rules. Research has produced increasing numbers of swarm intelligence algorithms, such as particle swarm optimisation (PSO) [2], ant colony optimisation (ACO) [3], the artificial fish-swarm algorithm [4], the free search algorithm [5], the human evolution model algorithm [6], the group search optimisation algorithm [7], the bees algorithm [8], bacterial foraging optimisation [9], the shuffled frog leaping algorithm [10], the fireworks algorithm [11], information feedback models [12], evolutionary multi-objectiveoptimization algorithms [13], the krill herd algorithm [14; 15], the harmony search algorithm [16], monarch butterfly optimization [17], the moth search algorithm [18], and cuckoo search [19]. Because biological structures are sophisticated, they have high degrees of adaptive capacity and strong collaborative capabilities, both in terms of evolution and behaviour [20]. The swarm intelligence algorithm is generally coordinated by a group of individuals (social insects, or particles). It has the ability to communicate (directly or indirectly) and interactively adapt to the environment so that the individual's adaptability to the environment becomes better and better, and a good approximate solution is gradually obtained for the global optimal solution to the problem. Presently, because of its high scalability and flexibility, researchers have applied swarm intelligence algorithm to many areas, such as scheduling [21], parameter estimation [22], multi-robot systems [23], and feature selection [24]. However, many bionic swarm intelligence algorithms need to set strict working conditions, such as the need to set a large number of individual behavior parameters (speed, inertia weight, acceleration coefficient, pheromone volatilization factor and intensity, etc. ), the need to provide the initial location [25], and so on. Only by knowing these parameters very well can one attain the optimal performance of the algorithm. In addition, some bionic swarm intelligence algorithms have high computational complexity, which requires a high computational cost [26]. Therefore, the swarm intelligence algorithm is still a hot research area. A large number of researchers are engaged in swarm intelligence research and actively apply the latest research results of swarm intelligence in application areas. Not only does the animal population have swarm intelligence, but the plant population in nature also has swarm intelligence. In 2006, inspired by the spatial pattern of plants in nature, a swarm intelligence algorithm called the bean optimisation algorithm (BOA) was proposed. Its main optimization mechanism is the spatial pattern model of plants and the elite beans selection mechanism, which is different from the existing bionic swarm intelligence algorithm. For example, in most wild leguminous plants, when the beans mature, the dry and hard pods burst in the sun, and the seeds are ejected around the plants. Most of the seeds will be distributed near the plants, and some of them will go far away or away from the plants for external reasons.