Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

A new metaheuristic can be developed by constructing from scratch, modifying the existing metaheuristics, or hybridizing some metaheuristics. This work presents a new metaheuristic: extended stochastic coati optimizer (ESCO). ESCO is developed by expanding the shortcoming coati optimization algorithm (COA). ESCO expands the number of searches and references used in COA. ESCO also implements a stochastic process for each unit to choose the searches that will perform. It differs from COA, which splits the population into two fixed groups, each performing its strategy. ESCO implements three sequential phases in every iteration. Two options can be chosen in every phase. ESCO has three references in its guided search: the global best unit, a randomly selected unit, and a randomized unit within the search space. In this work, ESCO is challenged to solve 23 classic functions and benchmarked with five shortcoming metaheuristics: guided pelican algorithm (GPA), puzzle optimization algorithm (POA), average subtraction-based optimizer (ASBO), and coati optimization algorithm (COA). The result presents the superiority of ESCO among five shortcoming metaheuristics by outperforming the GPA, POA, GSO, ASBO, and COA in solving 13, 21, 23, 16, and 13 functions, respectively. Through investigation, the multiple search approach is more effective than the single search approach.

“…Otherwise, the unit will avoid the reference. This selective movement can also be found in other shortcoming metaheuristics, such as KMA [18], TIA [28], NGO [19], and so on.…”

Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Extended Stochastic Coati Optimizer

2023

“…Mixed leader-based optimization (MLBO) [31], multi-leader optimization (MLO) [32], and hybrid leader-based optimization (HLBO) [33] are an example of metaheuristics that use the term leader as a metaphor. Fortunately, some metaheuristics, such as average subtraction-based optimization (ASBO) [34], golden search optimization (GSO) [35], and total interaction algorithm (TIA) [36], do not use metaphor and focus on their distinct mechanics for their novel contribution.…”

Section: Introductionmentioning

confidence: 99%

Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

2023

This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO). As the name suggests, ALO deploys two strategies to find the optimal solution. The central concept of ALO is to intensify guided searches as a required method. Then, a random search is performed only if the guided search fails to improve the current solution. ALO consists of four guided searches and one random search, performed in three phases: two mandatory and one optional. In the first phase, the guided search is conducted with the best global solution as the reference. In the second phase, the guided search is conducted with a randomly selected solution as the reference. The random search is performed in the third phase. Evaluating ALO, it was tested on 23 classic functions and benchmarked against five existing metaheuristics with known shortcomings: Mixed leader-based optimizer (MLBO), slime mould algorithm (SMA), golden search optimizer (GSO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The results indicate that ALO is highly competitive, outperforming MLBO, SMA, GSO, COA, and ZOA in solving 16, 16, 14, 10, and 9 functions respectively, and demonstrating ALO as a promising new metaheuristic.

“…Several metaheuristics use term leader that refers the reference used in their guided search, such as multi leader optimizer (MLO) [29], mixed leaderbased optimizer (MLBO) [30], hybrid leader-based optimizer (HLBO) [31], and so on. Several metaheuristics do not use metaphor; they use their core mechanics for their names, such as average subtraction-based optimizer (ASBO) [32], golden search optimizer (GSO) [33], total interaction algorithm (TIA) [34], and so on.…”

Section: Introductionmentioning

confidence: 99%

Quad Tournament Optimizer: A Novel Metaheuristic Based on Tournament Among Four Strategies

2023

This paper introduced a novel metaheuristic that is developed based on the tournament mechanism, namely quad tournament optimizer (QTO). As its name suggests, QTO proposes a new approach of metaheuristic in which there are four searches conducted by each agent in every iteration. These searches are: (1) searching toward the global best solution, (2) searching toward the middle between the global best solution and a randomly selected solution, (3) searching relative to a randomly selected solution, and (4) neighbourhood search around the corresponding solution and the global best solution. A solution candidate is generated by each search. Then, a tournament is carried out to find the best candidate. This strategy is novel because most of metaheuristic deploys only single search or multiple searches where each search is conducted sequentially. QTO is challenged to find the optimal solution of 23 classic functions. In this challenge, QTO is benchmarked against five shortcoming metaheuristics: marine predator algorithm (MPA), slime mould algorithm (SMA), golden search optimizer (GSO), hybrid pelican Komodo algorithm (HPKA), and guided pelican algorithm (GPA). The result indicates that QTO outperforms all these benchmark metaheuristics.