Self-adaptive Population Size Research Articles

Dynamic Artificial Bee Colony Algorithm with Hybrid Initialization Method

An improved basic artificial bee colony (ABC) algorithm with a self-adaptive technique, called dynamic ABC, is proposed. The dynamic ABC algorithm first uses a hybrid method combining the good-point-set method with the chaotic maps method to generate the initial population. Then, it applies self-adaptive population size at each generation, meaning that the population increases or decreases depending on some criteria, to enhance global convergence and avoiding local solutions. Experiments are carried out on a range of 10 popular benchmark functions. The results indicate that the dynamic ABC algorithm is superior to the basic ABC algorithm when considering both the speed and quality of the solution obtained.

A Firefly Algorithm With Self-Adaptive Population Size for Global Path Planning of Mobile Robot

In the simulation experiment of path planning of mobile robot based on firefly algorithm, it is found that the matching relationship between the number of fireflies and obstacles in the iterative process has significant conflict impacts on exploration ability and computational complexity of the algorithm. In order to solve the above problem, an optimal method of path planning based on firefly algorithm with self-adaptive population size is proposed. Firstly, the evaluation of degree of collision is established at the cost of avoiding collision. Based on the degree of collision of the population, two nonlinear functions are proposed to determine the population size. Then, individuals are added or deleted for the firefly population. Individuals are added randomly. The feasible solution and the infeasible solution are distinguished in firefly population, and delete the fireflies in the infeasible solution first when performing the eliminating operation. Finally, on the basis of the existing methods for dealing with infeasible paths, a coefficient that is adaptively adjusted according to the population size is introduced to control the degree to which the infeasible path approaches the feasible area. Compared with fixed population size firefly algorithm, the proposed algorithm has better performance in terms of solution stability, convergence speed and running time.

Parameters extraction of solar cells using modified JAYA algorithm

Solar energy has been receiving tremendous interest in this decade, due to their broad applications in areas of renewable energy sources. To identify the accurate modeling of current and voltage characteristics of solar cells will help to improve the performance of solar energy systems. However, the I-V characteristics are multimodal and complex non-linear equations making these optimization techniques to get trapped into local optima, low efficiency due to an initial solution far from the global optimal solution. Therefore, this paper proposes a modified JAYA (MJA) algorithm for estimating the solar cells parameters. In MJA, the modified solution updating phase by hybrid classical JAYA operator with differential evolution (DE/rand/1) operator, and self-adaptive population size are employed to improve global searching ability. In order to validate the feasibility and performance of the proposed MJA algorithm, experimental studies are carried out in identifying the parameters of a single diode, and double diode models and full comparison with different variants of JAYA, DE and other well-established algorithms in the last two years. Experimental results demonstrate that MJA can obtain better performance in terms of robustness and accuracy than all compared algorithms.

Alternative Constraint Handling Technique for Four-Bar Linkage Motion Generation

This paper proposes an extension of a new concept for four-bar path generation and a new penalty constraint handling technique from our previous work to a motion generation problem. The proposed technique could neglect the timing constraint for problems without prescribed timing, which is found to be an efficient technique for a four-bar path generation problem. Later, the remaining constraints are solved with a new constraint handling technique that also gives high performance. The technique is one kind of penalty function techniques. The present work proposes to combine the previous two methods for solving the four-bar motion generation problems. The comparative study, optimisation of motion generation problems with a new and a traditional penalty techniqueare solved by using self-adaptive population size teaching-learning based optimization (SAP-TLBO), is presented. In this study, three new motion generation test problems are used to test the proposed technique. The results show that the new technique gives acceptable results and can be applied with the motion generation problems without prescribed timing.

Alternative Constraint Handling Technique for Four-Bar Linkage Path Generation

This paper proposes an extension of a new concept for path generation from our previous work by adding a new constraint handling technique. The propose technique was initially designed for problems without prescribed timing by avoiding the timing constraint, while remain constraints are solving with a new constraint handling technique. The technique is one kind of penalty technique. The comparative study is optimisation of path generation problems are solved using self-adaptive population size teaching-learning based optimization (SAP-TLBO) and original TLBO. In this study, two traditional path generation test problem are used to test the proposed technique. The results show that the new technique can be applied with the path generation problem without prescribed timing and gives better results than the previous technique. Furthermore, SAP-TLBO outperforms the original one.

Four-bar linkage path generation through self-adaptive population size teaching-learning based optimization

This paper proposes a new variant of teaching-learning based optimization (TLBO) for optimal path synthesis of a four-bar linkage. The new TLBO algorithm is called self-adaptive population size teaching-learning based optimization (SAP-TLBO) which is altered from the original version by integrating a self-adaptive strategy for population sizing. A new concept of path generation is designed for problems without prescribed timing, which is proposed to avoid the timing constraint. In the first stage, two traditional path generation test problems are used to test the proposed technique and the new optimizer. Then, the new optimizer is used to solve five mechanical design problems. The results show that the new design concept for path synthesis gives significantly better results than the classical exterior penalty function technique, whereas the computational time is increased. SAP-TLBO is significantly superior to its original version and other meta-heuristics.

Multi-model function optimization based on immune clonal optimization with self-adaptive population size

In order to balance the impact of population size on the efficiency and effectiveness of the search algorithm,an immune clonal algorithm with self-adaptive population size for multi-modal function optimization problem was proposed.The change rules,magnitude and the detailed realization of population size were given.The change of self-adaptive population size with the evolutionary process was achieved.In addition,in terms of multi-modal function optimization characteristics,Baldwin learning was used as a local search strategy to enhance the search ability for the optimal solution.The experimental results show that the algorithm has better optimization ability,faster convergence and is more stable than existing algorithms.

Intelligent multi-objective decision-making model with RFID technology for production planning

A multi-objective production planning problem in the labor-intensive manufacturing industry is investigated. An intelligent and real-time multi-objective decision-making model is developed to provide timely and effective solutions for this problem by integrating RFID technology with intelligent optimization techniques, in which RFID technology is used to collect real-time production data, a novel (μ/ρ+λ)-evolution strategy process with self-adaptive population size and novel recombination operation is proposed and integrated with effective non-dominated sorting and pruning techniques to generate Pareto optimal solutions for real-world production. Experiments based on industrial data were conducted to evaluate the effectiveness of the proposed model. Experimental results show that the proposed model can effectively solve the investigated problem by providing production planning solutions superior to industrial solutions.

Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies

Differential Evolution is known for its simplicity and effectiveness as an evolutionary optimizer. In recent years, many researchers have focused on the exploration of Differential Evolution (DE). The objective of this paper is to show the evolutionary and population dynamics for the empirical testing on 3-Parents Differential Evolution (3PDE) for unconstrained function optimization (Teng et al. 2007). In this paper, 50 repeated evolutionary runs for each of 20 well-known benchmarks were carried out to test the proposed algorithms against the original 4-parents DE algorithm. As a result of the observed evolutionary dynamics, 3PDE-SAF performed the best among the preliminary proposed algorithms that included 3PDE-SACr and 3PDE-SACrF. Subsequently, 3PDE-SAF is chosen for the self-adaptive population size for testing dynamic population sizing methods using the absolute (3PDE-SAF-Abs) and relative (3PDE-SAF-Rel) population size encodings. The final result shows that 3PDE-SAF-Rel produced a better performance and convergence overall compared to all the other proposed algorithms, including the original DE. In terms of population dynamics, the population size in 3PDE-SAF-Abs exhibited disadvantageously high dynamics that caused less efficient results. On the other hand, the population size in 3PDE-SAF-Rel was observed to be approximately constant at ten times the number of variables being optimized, hence giving a better and more stable performance.

Self-adaptive population sizing for a tune-free differential evolution

The study and research of evolutionary algorithms (EAs) is getting great attention in recent years. Although EAs have earned extensive acceptance through numerous successful applications in many fields, the problem of finding the best combination of evolutionary parameters especially for population size that need the manual settings by the user is still unresolved. In this paper, our system is focusing on differential evolution (DE) and its control parameters. To overcome the problem, two new systems were carried out for the self-adaptive population size to test two different methodologies (absolute encoding and relative encoding) in DE and compared their performances against the original DE. Fifty runs are conducted for every 20 well-known benchmark problems to test on every proposed algorithm in this paper to achieve the function optimization without explicit parameter tuning in DE. The empirical testing results showed that DE with self-adaptive population size using relative encoding performed well in terms of the average performance as well as stability compared to absolute encoding version as well as the original DE.