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Enhanced Biogeography-Based Optimization (EBBO) technique is an improved version of BBO. BBO mainly uses the idea of probabilistically sharing features (Migration operator) among solutions based on the fitness values. The exploitation ability of BBO is good in comparison to many optimization techniques due to efficient sharing of information among solutions. However, migration operator creates similar...
This paper proposes fusion of synchronous germ computing (SGC) with twin swarm intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions...
This paper presents fusion of Bacterial Foraging with parameter free Particle Swarm Optimization (HBF-pfPSO). The proposed technique is used to enhance quality of global optima of multimodal functions. The authors propose two major modifications in Bacterial Foraging Optimization (BFO). Firstly, all bacteria position and direction are updated after all fitness evaluations instead of each fitness evaluation...
Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration...
In this paper, the authors propose a new evolutionary optimization technique i.e. modified biogeography-based optimization (MBBO). This technique is an improved version of BBO with each solution is directly encoded by floating point. BBO is a new bio-inspired and population based optimization algorithm for global optimization. The exploitation ability of BBO method is good but it lacks in exploration...
In this paper, the authors propose a new evolutionary optimization i.e. synchronous bacterial foraging optimization (SBFO). The SBFO can be used for optimization of multimodal and high dimensional functions. It also enhances computational throughput and global search capability. The convergence of original BFO to the optimum value is very slow and its performance is also heavily affected with increased...
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