Crisscross optimization algorithm and its application

Meng, Anbo; Chen, Yu Cheng; Yin, Hao; Chen, Sizhe

doi:10.1016/j.knosys.2014.05.004

Cited by 156 publications

(70 citation statements)

References 46 publications

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“…The crisscross optimization algorithm (CSO) is a recent evolutionary algorithm inspired by Confucian doctrine of gold mean and the crossover operation of genetic algorithm [26]. Compared to other heuristic algorithms, the CSO algorithm has a huge advantage in solution accuracy and convergence speed when addressing complex optimization problems [26,27].…”

Section: Determination Of Feasible Solutions Using Csomentioning

confidence: 99%

“…Compared to other heuristic algorithms, the CSO algorithm has a huge advantage in solution accuracy and convergence speed when addressing complex optimization problems [26,27]. The CSO algorithm is mainly made of three components: the horizontal crossover, the vertical crossover and the competitive operator.…”

Section: Determination Of Feasible Solutions Using Csomentioning

confidence: 99%

“…Each pair of particles reproduces two moderation solutions according to Equation (12). Through the horizontal crossover operator, new solutions can be searched in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability, which had been proved in [26]. This search mechanism gifts CSO with powerful global search ability.…”

Section: Horizontal Crossovermentioning

confidence: 99%

“…The role of vertical crossover search is to facilitate some stagnant dimensions of the population and escape from dimension premature convergence. Unlike horizontal crossover, only a few paired dimensions are chosen to perform the vertical crossover operation because that about 10%-40% of the dimensions in the swarm may be simultaneously trapped into stagnancy when optimizing the multimodal functions [26]. Therefore, the vertical crossover probability P vc is suggested to be set in the range [0.2, 0.8] in [26].…”

Section: Horizontal Crossovermentioning

confidence: 99%

“…Unlike horizontal crossover, only a few paired dimensions are chosen to perform the vertical crossover operation because that about 10%-40% of the dimensions in the swarm may be simultaneously trapped into stagnancy when optimizing the multimodal functions [26]. Therefore, the vertical crossover probability P vc is suggested to be set in the range [0.2, 0.8] in [26]. Similar to the horizontal crossover search, the new solutions should compete with the parent particle and those solutions that outperform their competitor can survive.…”

Section: Horizontal Crossovermentioning

confidence: 99%

See 4 more Smart Citations

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Peng

Zheng

et al. 2015

Energies

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Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem.

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Section: Determination Of Feasible Solutions Using Csomentioning

confidence: 99%

Section: Determination Of Feasible Solutions Using Csomentioning

confidence: 99%

Section: Horizontal Crossovermentioning

confidence: 99%

Section: Horizontal Crossovermentioning

confidence: 99%

Section: Horizontal Crossovermentioning

confidence: 99%

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Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Peng

Zheng

et al. 2015

Energies

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A novel design approach for indoor environmental quality based on a multiagent system for intelligent buildings in a smart city: Toward occupant's comfort

Verma

Prakash

Kumar

et al. 2022

Env Prog and Sustain Energy

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In a smart city, energy usage for sustaining indoor environmental quality (IEQ) and occupant comfort levels frequently clash. This research aims to design a novel approach to solve the conflict between the use of power and the comfort of the citizens. A multi-purpose problem for minimum consumption of energy as well as maximum occupant's ease within the energy-efficient construction for smart city as a future has been formulated. Across suggested design method, three variable namely temperature ( C), illumination level (lux), as well as Carbon dioxide (CO 2 -ppm) have been undertaken for occupant's comfort. The Crisscross Search Particle Swarm Optimization (CSPSO), a revolutionary solution for the design method, has been applied to search for the optimal value of environmental factors (temperature, light level, CO 2 ) in relation to user-set preferences. In the heuristic method, the augmented particle swarm optimization strategy updates an initial solution before updating the best local solutions through length and breadth crossover operators. The CSPSO approach search explores the search space in all dimensions and reduces the stagnation trouble by through length and breadth crossover operators. The temperature, lighting, CO 2 sensors data, and user-defined parameters are all inputs to the CSPSO algorithm, and the optimized parameters are the outputs of the optimization method. Such optimized parameters will become an input to the fuzzy controllers that adjust various actuators' status according to the users' comfort. Utilizing the CSPSO technique, 22.74% energy of 625.372 kWhr energy was saved during a day, and the comfort level has increased from 0.51692 to 0.75685. Therefore, the proposed novel design approach is acceptable in managing building energy consumption by achieving maximum comfort.

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CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Ghasemi

Ghavidel

Aghaei

et al. 2018

Int Trans Electr Energ Syst

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Summary This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110‐unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well‐organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems.

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Crisscross optimization algorithm and its application

Cited by 156 publications

References 46 publications

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

A novel design approach for indoor environmental quality based on a multiagent system for intelligent buildings in a smart city: Toward occupant's comfort

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

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