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An improved multi-objective particle swarm optimization (IMOPSO) is presented because of the different demand for decision and state variables in engineering optimizations. IMOPSO adopts a new method of dynamic change about acceleration coefficients based on sine transform to improve the ability of global search in early period and the local search ability in the last runs of the algorithm. To expand...
The increase in real power demand on the distribution side of power system affects the cost of power generation. The main objective is to schedule the total demand among all the units in a thermal plant with minimum fuel cost by applying an intelligent technique. There are many conventional and intelligent techniques which are in practice for the economic dispatch of thermal units. In recent days...
It is widely known that particle swarm optimization (PSO) has some drawbacks, especially it loses diversity easily. In order to solve this problem, some improved PSOs were proposed which update velocity according to diversity. However, some important information about particles is still not sufficiently utilized such as fitness values. As a gradient descent method, backpropagation (BP) algorithm is...
For numerical high-dimensional real parameter optimization problem, a new clonal selection memory algorithm is proposed in the paper by introducing a short-term memory mechanism into the optimization algorithm. Through non-genetic information storage and guiding the subsequent evolution the algorithm can effectively increase the local convergence rate. On the other hand, the search depth threshold...
In 2008 the WABT Academy (NGO founded under the aegis of UNESCO in Paris in 1997), has promoted the launching of the WABIT – World Association of Bio Info Technologies – World Association of Bio Info Technomics (French Denomination "Association Mondiale des Sciences BioInformatiques et BioTechnologiques", Italian Denomination – "Associazione Mondiale delle Scienze BioInformatiche e...
In Shuffled Frog Leaping Algorithm (SFLA), the worst frog' position is improved based on the experiences of the best local or global frog, in two steps separately. In this Algorithm, discovering more useful information of previous search experience through designing learning strategies is a challenging research. While each of these experiences may has better value on some dimensions, the other one...
Biogeography-based optimization (BBO) is a new biogeography inspired algorithm for global optimization, which has shown impressive performance on many popular benchmarks. However, there are still some open research aspects that need to be addressed for BBO. In this paper, we extend the original BBO to iCPBBOCO algorithm (an improved Cosine-Probability chaotic BBO). Furthermore, in order to further...
This work deals with the development of a new ‘intelligent monkey king evolution (IMKE) algorithm’ for maximum power peak (MPP) detection under partially shaded solar photovoltaic (SPV) conditions. The major advantages of the developed technique are simple in implementation, population free, extremely less memory requirement, zero algorithms constant dependence, fast convergence and perfect structure...
Recently, Search Based Software Testing (SBST) research has gained much attention in producing the optimal solution for the optimization problem by automating the test data generation for the branch coverage criterion. Particle Swarm Optimization (PSO) has been emerged for obtaining optimal solution for the test data generation problem because of its easy implementation, fast convergence and few parameters...
This paper proposes a novel maximum power point tracking (MPPT) algorithm for photovoltaic (PV) power generation systems under non-uniform illumination. The proposed MPPT algorithm is composed of gravitational search algorithm (GSA) and traditional perturb and observe (P&O) method. In the initial stages of tracking, the power-voltage (P-V) curve is scanned through GSA and the best solution obtained...
This paper conducts a comparative study between an improved variants of genetic algorithm (GA) and a swarm intelligence algorithm (SIA), which are the Dual population Genetic Algorithm (DPGA) and Artificial Bee Colony (ABC) Algorithm. DPGA is a multi-population genetic algorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population...
The time an offspring should live and remain into the population in order to evolve and mature is a crucial factor of the performance of population-based algorithms both in the search for global optima, and in escaping from the local optima. Offsprings lifespan influences a correct exploration of the search space, and a fruitful exploiting of the knowledge learned. In this research work we present...
Cooperative coevolution framework is an effective strategy to deal with large scale optimization problems. However, most studies on cooperative coevolution framework utilize the same optimizer for all subcomponents, which may not be effective enough. In this paper, we propose a novel multi-optimizer cooperative coevolution method for large scale optimization problems which randomly chooses an optimization...
With the help of the cooperative co-evolution, differential evolution (DE) has been applied successfully from low-dimensional problems to large scale optimization. In this paper, we propose a preferred learning cooperative coevolution DE algorithm (LDECC-DG) which focuses on the basic optimizer for large scale optimization using cooperative coevolution. The proposed LDECC-DG builds on the differential...
In evolutionary algorithms, it is difficult to balance the exploration and exploitation. Usually, global search is utilized to find promising solutions, and local search is beneficial to the convergence of the solutions in the population. Combing different search strategies is a promising way to take advantages of different methods. Following the idea of DE/EDA, this paper proposes another way to...
Artificial fish swarm algorithm (AFSA) is a newly proposed swarm intelligent optimization algorithm. It is proved to be a promising approach to complex engineering problems, yet still there exist some defects of this algorithm. To solve the problem that AFSA has a low rate of convenience, low optimization precision, premature convergence and poor ability of balancing exploitation and exploration,...
Particle swarm optimization (PSO) is a heuristic stochastic evolutionary algorithm. However, standard PSO exists unbalanced exploitation and exploration, lower convergence speed. An improved technique is introduced into the standard PSO with adaptive computation of the inertia weights. After every iteration, a new competition with a random swarm is operated to jump out of the local optimum. Four benchmark...
Central Force Optimization (CFO) and Gravitational Search Algorithm (GSA) have been used in various fields. This article compared CFO and GSA and exhaustively analyzed the difference. Their performance on solving unimodal and multimodal functions is analyzed by a series of test functions. Then, conclusions that characteristics of the two methods are made, and technical guidance for the selection of...
Big Bang-Big Crunch Algorithm (BBBC) is a theoretical framework of analyzing a set of alternatives to reach the best outcome. An algorithm of Hybrid BBBC with the objective to minimize the makespan is presented to tackle the job-shop scheduling problem. The initial solutions to the typical NP-hard problem are generated according to different heuristic strategies in a combination way. Modified BB strategy...
Based on the characteristic of autonomous underwater vehicle path planning, the method of path planning was analyzed by genetic algorithm. Firstly, by means of grid, plan space was modeled into two markers. Best path was searched by genetic algorithm. Method of giving birth to initial groups was improved. Sufficiency function of path planning was given. Chamfer operator in genetic algorithm was imported...
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