Poor and rich optimization algorithm: A new human-based and multi populations algorithm

Moosavi, Seyyed Hamid Samareh; Bardsiri, Vahid Khatibi

doi:10.1016/j.engappai.2019.08.025

Cited by 288 publications

(54 citation statements)

References 34 publications

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“…The parabolic equation of the Cycloid is shown in Eq. (20), and the two-dimensional image of the Cycloid is shown in Fig. 9.…”

Section: ) Epitrochoid (Ep)mentioning

confidence: 99%

“…The concept of bionic algorithm was originally the Genetic Algorithm (GA) [5] proposed by Professor Holland, Popular algorithms in this field include: Particle Swarm Optimization (PSO) [6], [7], Differential Evolution (DE) [8], Evolution Strategy (ES) [9], Artificial Bee Colony algorithm (ABC) [10], [11], Cuckoo Search Algorithm (CS) [12], Grey Wolf Optimizer (GWO) [13], Dolphin Echolocation (DE) [14], Dragonfly Algorithm (DA) [15], Grasshopper Optimization Algorithm (GOA) [16], Bat Algorithm (BA) [17], Tree Growth Algorithm (TGA) [18], Barnacles Mating Optimizer (BMO) [19], Poor and Rich Optimization (PRO) [20], Pathfinder Algorithm (PFA) [21], Galactic Swarm Optimization (GSO) [22], Farmland fertility [23], Falcon Optimization Algorithm (FOA) [24], SailFish Optimizer (SFO) [25], Meerkats-inspired algorithm (MEA) [26], Owl Optimization Algorithm (OOA) [27], Interactive Search Algorithm (ISA) [28], Coyote Optimization Algorithm (COA) [29], Cheetah Based Optimization Algorithm (CBA) [30], etc. So far, there are many complex optimization problems that are difficult to get the best solution.…”

Section: Introductionmentioning

confidence: 99%

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Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns

et al. 2020

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Ant Lion Optimizer (ALO) is a new meta-heuristic algorithm that simulates the ant lion predator mechanism in nature. Five main steps of hunting include: random walks of ants, building traps, trapping in antlion's pits, sliding ants towards antlion, catching prey and re-building pits. As the predator radius of antlion decreases with the number of iterations, there is an unbalanced between the ant lion optimizer between exploration and exploitation, and it is easy to fall into the local optimal solution. An improved ant lion optimizer based on spiral complex path searching pattern is proposed, where eight spiral paths (Hypotrochoid, Rose spiral curve, Logarithmic spiral curve, Archimedes spiral curve, Epitrochoid, Inverse spiral curve, Cycloid, Overshoot parameter setting of the spiral) searching strategies were adopted to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The proposed algorithm can accelerate the convergence speed of ALO and improve its performance. The algorithm is verified by simulation experiments in three parts. Firstly, 28 function optimization problems were adopted to test the optimization performance of the improved ALO. Secondly, it is applied to the lightest design engineering problem of pressure vessels. Finally, the spiral complex path searching patterns are introduced into the muti-objective ALO and 4 typical muti-objective functions are optimized. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved algorithm can better solve function optimization, classical engineering problems with constraints and multi-objective function optimization problems. The improved ALO based on the spiral complex path searching mode has the characteristics of balanced exploration and exploitation, fast convergence speed and high precision.INDEX TERMS Ant lion optimizer, spiral complex path, function optimization, constrained optimization, muti-objective optimization.

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“…The parabolic equation of the Cycloid is shown in Eq. (20), and the two-dimensional image of the Cycloid is shown in Fig. 9.…”

Section: ) Epitrochoid (Ep)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns

et al. 2020

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“…The design of the controllers will be performed for the operation condition using different optimization techniques which are: PRO [20], SELO [19], GOZ [18], and PSO [17]. Then, the rest of the operating conditions are performed using PRO only.…”

Section: Optimization Problem Definitionmentioning

confidence: 99%

“…In [17], the particle swarm algorithm (PSO) is presented, in [18] the grasshopper optimization algorithm (GOZ) is presented, and in [19] socio evolution & learning optimization (SELO) is presented. Poor and rich optimization algorithm (PRO) was presented for the first time in [20] by Moosavi and Bardsiri as a novel human meta-population approach for solving engineering optimization problems.…”

Section: Introductionmentioning

confidence: 99%

5G Poor and Rich Novel Control Scheme Based Load Frequency Regulation of a Two-Area System with 100% Renewables in Africa

Fayek

2020

Fractal Fract

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Remote farms in Africa are cultivated lands planned for 100% sustainable energy and organic agriculture in the future. This paper presents the load frequency control of a two-area power system feeding those farms. The power system is supplied by renewable technologies and storage facilities only which are photovoltaics, biogas, biodiesel, solar thermal, battery storage and flywheel storage systems. Each of those facilities has 150-kW capacity. This paper presents a model for each renewable energy technology and energy storage facility. The frequency is controlled by using a novel non-linear fractional order proportional integral derivative control scheme (NFOPID). The novel scheme is compared to a non-linear PID controller (NPID), fractional order PID controller (FOPID), and conventional PID. The effect of the different degradation factors related to the communication infrastructure, such as the time delay and packet loss, are modeled and simulated to assess the controlled system performance. A new cost function is presented in this research. The four controllers are tuned by novel poor and rich optimization (PRO) algorithm at different operating conditions. PRO controller design is compared to other state of the art techniques in this paper. The results show that the PRO design for a novel NFOPID controller has a promising future in load frequency control considering communication delays and packet loss. The simulation and optimization are applied on MATLAB/SIMULINK 2017a environment.

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“…The last category, called human-based algorithms, has worked on emulating human behaviors, for proposing other algorithms with different methodology; including Teaching Learning Based Optimization(TLBO) [41], Harmony search (HS) [42], League Championship Algorithm (LCA) [43], Group Counseling Optimization (GCO) [44,45], Mine Blast Algorithm (MBA) [46], Seeker Optimization Algorithm (SOA) [47], Soccer League Competition (SLC) algorithm [48,49], Firework Algorithm [50], and many others [51].…”

Section: Introductionmentioning

confidence: 99%

Modified Flower Pollination Algorithm for Global Optimization

et al. 2021

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In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.

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Poor and rich optimization algorithm: A new human-based and multi populations algorithm

Cited by 288 publications

References 34 publications

Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns

Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns

5G Poor and Rich Novel Control Scheme Based Load Frequency Regulation of a Two-Area System with 100% Renewables in Africa

Modified Flower Pollination Algorithm for Global Optimization

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