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Table 6 Selected meta-heuristic optimization

From: A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

Publication year

Optimization techniques

Advantage

Disadvantage

Biological behavior

2017

Polar bear optimization algorithm (PBO)

∙ With the help of this strategy, which continually eliminates ideas that are not moving forward successfully, excellent solutions may be given a second opportunity (Mirkhan and Celebi 2022)

∙ The algorithm finds the ideal region quickly and finds an optimal solution with fewer iterations (Amatullah et al. 2021)

∙ Slow convergence

Simulates polar bear behavior under extreme arctic circumstances

2017

Satin Bowerbird Optimizer (SBO)

∙ When compared to SA, PSO, FA, and RSM, it exhibits great performance in terms of cost (Chintam and Daniel 2018)

∙ Low optimization precision and sluggish convergence (Li et al. 2022)

Simulates a male adult’s behavior during breeding Slender Bowerbird

2016

Sine cosine algorithm (SCA)

∙ It outperforms PSO, FA, GA, BA, and GSA in terms of speed, convergence, exploration, exploitation, and local optima avoidance while handling multi-objective optimization problems (Abualigah and Diabat 2021)

∙ Its rate of convergence is slow and it is simple to enter the local optimum (Wang and Lu 2021)

Trigonometric sine and cosine functions serve as inspiration

2016

Whale optimization algorithm (WOA)

∙ Easy to understand and uses few control parameters (Li et al. 2019)

∙ its convergences speed is low and easily falls into the local optimum (Guo et al. 2020)

Motivated by humpback whales’ hunting habits

2016

Mosquito Host Seeking (MSO)

∙ Multiple objective optimizations, large-scale distributed parallel optimization, problem-solving efficacy, and appropriateness for complicated environments are just a few benefits of the MHS technique (Feng et al. 2016)

∙ Slow performance (Wang and Lin 2017)

mimics the actions of a female anthropophagous mosquito looking for a host

2016

Crow Search Algorithm (CSA)

∙ CSA offers benefits such as a quick convergence rate, simplicity, and programming convenience (Bahamish et al. 2021)

∙ These drawbacks include the algorithm’s early convergence, trapping in local minima, limited capacity to explore certain sorts of challenging problems, and sometimes failure to discover the best solution (Bahamish et al. 2021)

It enables the storage of extra food and retrieval of it when required, mimicking crow behavior

2015

Moth Fly Optimization (MFO)

∙ The benefits of having a small number of setting parameters, being simple to understand and implement, and having a fast convergence (Li et al. 2020)

∙ Problems with the MFO algorithm include early convergence, a lack of population variety, the trapping of local optima, and an imbalance between exploration and exploitation (Nadimi-Shahraki et al. 2021)

Inspire the natural biological behavior of moths fighting flames

2014

Colliding Body Optimization (CBO)

∙ This method is more stable and converges than the BBO, GSA, DE, and PSO algorithms (Liu et al. 2021a)

∙ The CBO method is still limited by its search accuracy and is susceptible to a subsequent iteration of falling into the local optimum solution (Liu et al. 2021a)

Inspired by the collision of two one-dimensional objects

2014

Grey Wolf Optimizer (GWO)

∙ GWO offers the benefits of fewer parameters, straightforward concepts, and straightforward implementation as compared to conventional optimization algorithms like PSO and GA (Liu et al. 2021c)

∙ The drawbacks of GWO include its sluggish convergence rate, poor solution precision, and propensity to easily enter the local optimum (Liu et al. 2021c)

Emulate the actions of grey wolves in the wild

2013

Social Spider Optimization (SSO)

∙ It displays excellent classification results and is 76% more effective than ALO, SFLA, FPA, BA, and CSO (Luque-Chang et al. 2018; Suruli and Ila 2020)

∙ It could experience early convergence as a result of choosing the wrong local spiders to reach the global solution. (Tamilarasi et al. 2021)

Simulating social spiders’ foraging behavior

2010

Bat Algorithm (BA)

∙ It demonstrates flexibility and simplicity (Yang and He 2013)

∙ It converges swiftly in the beginning and gradually slow down (Liu et al. 2021b)

Drawing inspiration from the foraging habits of microbats

2009

Cuckoo Optimization Algorithm (COA)

∙ It has several benefits, including being simpler to use and requiring fewer tuning parameters (Liping et al. 2018)

∙ It has been shown to have a sluggish rate of convergence and to very quickly settle into local optimum solutions (Liping et al. 2018)

based on cuckoo breeding and spawning

2008

Biogeography Based Optimization

∙ Specifically, it is used for high-dimension problems with multiple local optima

∙ It performs better than PSO, GA, DE, ACO, and ES in terms of sensory problems (Simon 2008)

Habitat doesn’t take its resulting fitness into account when importing the characteristics, which leads to the development of a large number of unworkable solutions. BBO is terrible at using the solutions. There is no mechanism for picking the best members from each generation (Simon 2008)

∙ By using species movement, it accomplishes information sharing

2007

Firefly Algorithm (FA)

∙ The experimental findings indicate that the suggested method performs better than DE and PSO in terms of avoiding local minima and speeding up convergence (Zhang et al. 2016)

∙ Because they are local search algorithms, one of their key drawbacks is the likelihood of becoming stuck in local optima (Zhang et al. 2016)

The algorithm imitates how fireflies communicate by flashing their lights

2007

Imperialist Competitive Algorithm (ICA)

∙ Demonstrate the effectiveness and capacity for locating the optimum. Amazingly, it outperforms other algorithms like GA, ABC, PSO, and HEICA in terms of performance (Mitras and Sultan 2013)

∙ If this movement persists, the colonizer or imperialist may eventually completely colonize or occupy the colony (Babaei Keshteli et al. 2021)

Motivated by imperialist rivalry

2006

Invasive Weed Optimization (IWO)

∙ It now has a high accuracy rate and a quick convergence rate (Misaghi and Yaghoobi 2019)

∙ It converges swiftly in the beginning and later the rate of convergence declines (Fang et al. 2020)

Influenced by weed colony behavior

2006

Cat Search Algorithm (CSA)

∙ (CSA) beat the DA, BOA, and FDO and offers a unique modeling method for the exploration and exploitation phases (Ahmed et al. 2020)

∙ Its convergence accuracy and speed are impacted (Songyang et al. 2022)

Mimic the actions of cats

2006

Shuffled Frog-Leaping Algorithm (SFLA)

∙ When compared to GA. SFLA is a powerful tool for tackling combinatorial optimization issues (Eusuff et al. 2006)

∙ It exhibits slow convergence, a propensity to settle for the neighborhood’s optimal solution, and premature convergence (Eusuff et al. 2006)

Inspired by frogs’ social interactions

2005

Artificial Bee Colony (ABC)

••∙ The method is simple to use, capable of both local and global searches, and open to hybridization with other algorithms (Yuce et al. 2013)

∙ Randomization is used. There are several tuning settings for the algorithm (Yuce et al. 2013)

∙ It converges slowly throughout the search process and is susceptible to early (Zhao et al. 2015)

Based on the intelligent foraging bee

2002

Bacterial foraging optimization (BFO)

∙ It has good competitive performance in addressing unconstrained optimization issues compared to DE, GA, and PSO (Hernández-Ocana et al. 2013)

∙ Due to weak bacterial interactions and the difficulty of balancing the exploratory abilities, it falls to the local optimal solution (Chen et al. 2020)

∙ Replicates bacteria’s foraging behavior

2001

Harmony Search (HAS)

∙ Although the harmony search algorithm, which attempts to mimic the improvisation process of musicians, has better global optimization capability and an excellent combined power with other algorithms (Tian and Zhang 2018)

∙ Its drawbacks include randomness, instability, and difficulty in balancing between exploration and exploitation

(Tian and Zhang 2018)

Which tries to mimic the improvisation process of musicians

1997

Differential Evolution (DE)

∙ It frequently provides superior results to those produced by GA and other evolutionary algorithms

∙ It is effective at locating true global minima, converges quickly, and requires few control parameters (Eltaeib and Mahmood 2018; Karaboga and Cetinkaya 2004)

∙ Difficulty in estimating the best ratios between Cauchy mutation-generated solutions and uniform distribution solutions (Ahmad et al. 2021)

The population-based heuristic global optimization technique

1995

Particle Swarm Optimization (PSO)

It is straightforward to implement, robust to control parameters, and computational (Lee and Park 2006)

Early convergence, memory-intensive update rates, and subpar solutions (Rahman et al. 2016)

The movement of bird flocks and schooling fish served as inspiration

1994

Cultural Algorithm (CA)

It has a higher convergence rate than PSO and uses the database it built to guide the search for each cultural species (Kuo and Lin 2013)

CA is incompetent at resolving multi-extremal optimization issues (Muhamediyeva 2020)

Use a foundational collection of knowledge sources, each relating to information learned from studying various animal species

1992

Ant Colony Optimization(ACO)

It is simple to combine with other techniques, has strong robustness and a good distributed calculative mechanism, and has demonstrated strong performance when solving challenging optimization problems (Jaiswal and Aggarwal 2011)

It falls victim to local traps easily and takes a long time (Samsuddin et al. 2018)

Replicated the actions of ants

1986

Artificial immune system (AIS)

AIS is very good at pattern recognition, learning, and associative memory (Zhang et al. 2004)

It is difficult to combine AIS with other machine languages (Chanal et al. 2021)

It is derived from the ideas that the immune system of vertebrates like humans inspired