Enhanced multi-objective mountain gazelle optimization via modified adaptive weight approach for construction time-cost trade-off problems

Tayfun Dede; Mohammad Azim Eirgash; Andrzej Kysiak; Hacı Abdullah Uçan

doi:10.17512/bozpe.2026.15.04

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Enhanced multi-objective mountain gazelle optimization via modified adaptive weight approach for construction time-cost trade-off problems

Construction of Optimized Energy Potential

AHEAD OF PRINT

By: Tayfun Dede , Mohammad Azim Eirgash , Andrzej Kysiak and Hacı Abdullah Uçan

Open Access

|May 2026

Figures & Tables

Flowchart of the MAWA-MGO algorithm for TCTP (own research)

Initialization of the mountain gazelle optimizer’s phases (own research)

Visualization of the mountain gazelle optimizer’s phases (own research)

Represents the comparison of the obtained solutions (own research)

Graphical representation of the performance metric comparisons for the proposed algorithm with other algorithms for the 19 activity project (own research)

Box plot for the comparison algorithms (own research)

Options for 19 activity projects with three modes (own research)

Description		Mode 1		Mode 2		Mode 3
Act. No	Predecessor Act.	T	C	T	C	T	C
1	–	3	1326324	5	1032641	8	923634
2	1	5	1026756	9	914737	9	849627
3	1	14	118404	15	107573	15	103734
4	2, 3	10	1626972	13	1472345	14	1391235
5	1	16	1026756	19	962438	20	923593
6	3, 5	13	117144	14	102312	14	101231
7	5	10	1626972	14	1531267	16	1492451
8	4, 6	7	118404	8	109212	14	92101
9	7, 8	5	1200036	9	1026384	14	885738
10	9	6	1626972	8	1512438	9	1442733
11	9	9	759780	11	683412	12	652846
12	10, 11	20	815964	25	753578	25	713580
13	10, 11	4	180744	5	162358	8	136489
14	12	12	783984	13	732678	15	697896
15	13	18	180744	20	114678	20	101569
16	13, 14	10	783984	12	735675	20	634568
17	16	8	180744	9	163848	12	136385
18	15, 16	11	674952	13	643782	13	618904
19	17, 18	4	66060	5	63321	6	61456

Pareto-front solutions of 19 activity TCTP problem (own research)

Sol. No	Agarwal et al. (2024)		This study
	MOPSO		Plain MGO		MAWA-MGO
	PCT	PCC	PCT	PCC	PCT	PCC
1	124	13653118	114	12858804	110	13131802
2	128	13440491	119	12634927	111	12995830
3	130	13044162	139	12731970	124	13156641
4	132	12976175	123	13391744	128	13043361
5	133	12885800	120	12608784	125	12868925
6	134	12858263	147	12012051	138	12589462
7	136	12842530	120	12608784	116	12782002
8	138	12801272	110	13238207	119	12633846
9	139	12761554	111	13054246	137	12717131
10	141	12539569	113	12926509	136	12963368
NOP	100		40		40
NOI	200		130		130
NFE	20000		5440		5440

Correlation between PCT and PCC (own research)

Variable 1	Variable 2	Pearson Correlation Coefficient (r)	p-value
PCT	PCC	−0.27, a weak negative correlation, not statistically significant	0.447

Statistical analysis for PCC (own research)

Algorithm	Mean	Std Dev	Min	Median	Max
MOPSO	12,980,293	330,908	12,539,569	12,872,031	13,653,118
Plain MGO	12,806,603	387,545	12,012,051	12,795,387	13,391,744
MAWA-MGO	12,888,236	202,091	12,589,462	12,916,146	13,156,641

Statistical analysis for PCT (own research)

Algorithm	Mean	Std Dev	Min	Median	Max
MOPSO	133.50	5.25	124.00	133.50	141
Plain MGO	121.60	12.20	110.00	119.50	147
MAWA-MGO	124.40	10.43	110.00	124.50	138

Performance comparison of the proposed models and plain MGO, MAWA-MGO and MOPSO algorithms for the 19 activity project (own research)

Algorithms	NPFs	NFE	Sp	HV
MOPSO (Agarwal et al., 2024)	10	1^*	0.912	0.621
MAWA-MGO (this study)	10	0.27	0.778	0.697
Plain MGO (this study)	10	0.27	0.891	0.630

Three high-performing solutions for the 19 activity project using CDR mechanism (own research)

MAWA-MGO		Crowding distance rank (CDR)	CDR order
PCT	PCC	Crowding distance rank (CDR)	CDR order
110	13,131,802	Inf (∞)	1
111	12,995,830	0.971	2
138	12,589,462	0.897	3

References

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Articles in this issue

DOI: https://doi.org/10.17512/bozpe.2026.15.04 | Journal eISSN: 2544-963X | Journal ISSN: 2299-8535

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Language: English

Published on: May 19, 2026

Published by: Technical University in Czestochowa

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

time-cost-trade-off problems,

modified adaptive weight approach,

mountain gazelle algorithm,

multi-criteria decision-making tool

Related subjects:

Architecture and design,

Architecture and design, other,

Engineering,

Civil engineering,

Materials,

Environmental engineering

© 2026 Tayfun Dede, Mohammad Azim Eirgash, Andrzej Kysiak, Hacı Abdullah Uçan, published by Technical University in Czestochowa
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 License.

AHEAD OF PRINT

Enhanced multi-objective mountain gazelle optimization via modified adaptive weight approach for construction time-cost trade-off problems

Figures & Tables

Fig. 1.

Fig. 2.

Fig. 3.

Fig. 4.

Fig. 5.

Fig. 6.

Options for 19 activity projects with three modes (own research)

Pareto-front solutions of 19 activity TCTP problem (own research)

Correlation between PCT and PCC (own research)

Statistical analysis for PCC (own research)

Statistical analysis for PCT (own research)

Performance comparison of the proposed models and plain MGO, MAWA-MGO and MOPSO algorithms for the 19 activity project (own research)

Three high-performing solutions for the 19 activity project using CDR mechanism (own research)

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